Model4_based on labeled data.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.1"},"colab":{"name":"SemiSupervised.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"code","metadata":{"id":"umudtVwO94nR","colab_type":"code","outputId":"83e7f643-744d-4c16-ab5c-09f25aff7f0d","executionInfo":{"status":"ok","timestamp":1587359005591,"user_tz":-480,"elapsed":31222,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ohQlypRn8cVg","colab_type":"code","colab":{}},"source":["import numpy as np\n","import pandas as pd\n","import re\n","import jieba\n","from sklearn.feature_extraction.text import CountVectorizer\n","from sklearn.feature_extraction.text import TfidfTransformer\n","from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import *\n","from sklearn.svm import SVC\n","from sklearn.model_selection import GridSearchCV\n","import xgboost as xgb\n","from sklearn.naive_bayes import MultinomialNB"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pspN5K8a-Lib","colab_type":"code","colab":{}},"source":["import os\n","os.chdir('/content/drive/My Drive/5153 project/')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FgcM-_ny8cVo","colab_type":"code","colab":{}},"source":["df = pd.read_csv('random_3000_with_label.csv')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"de3p3I7m8cV5","colab_type":"code","outputId":"3e1e2e34-eeef-4024-e2a4-00b27274eb2c","executionInfo":{"status":"ok","timestamp":1587359529087,"user_tz":-480,"elapsed":883,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":462}},"source":["df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Index</th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>manually_label</th>\n","      <th>sentiment_baseline</th>\n","      <th>senti_by_refined_snownlp</th>\n","      <th>senti_by_jieba_sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10289</td>\n","      <td>2670985301</td>\n","      <td>2020-02-08 23:52</td>\n","      <td>抗击疫情 我们誓死不退（祝洛东）  #武汉加油##抗击新型肺炎我们在行动##抗疫行动##万众...</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>33252</td>\n","      <td>5622277724</td>\n","      <td>2020-03-06 23:22</td>\n","      <td>#甘肃新增17例境外输入新冠肺炎# 又来[费解]晕死真的是</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>15869</td>\n","      <td>5936449524</td>\n","      <td>2020-02-15 23:54</td>\n","      <td>#新冠肺炎已开始在日本流行# 我靠</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>14422</td>\n","      <td>2211096821</td>\n","      <td>2020-02-13 23:59</td>\n","      <td>日本再确诊1例新冠肺炎病例 总确诊数已达251例 http://t.cn/A6hGfqoZ</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23309</td>\n","      <td>6303064815</td>\n","      <td>2020-02-23 23:59</td>\n","      <td>//@微博辟谣:#微博辟谣# 金银潭医院：军运会五外籍运动员患的都是疟疾，与新冠肺炎无关</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Index     user_id  ... senti_by_refined_snownlp senti_by_jieba_sentiment\n","0  10289  2670985301  ...                        1                        1\n","1  33252  5622277724  ...                       -1                        1\n","2  15869  5936449524  ...                        0                        1\n","3  14422  2211096821  ...                       -1                        1\n","4  23309  6303064815  ...                       -1                        1\n","\n","[5 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"scrolled":false,"id":"MQfGzLIW8cWD","colab_type":"code","outputId":"97f90dc1-80cd-4e59-eecf-182ca0c9ced2","executionInfo":{"status":"ok","timestamp":1587361997444,"user_tz":-480,"elapsed":612,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":289}},"source":["df.drop(['sentiment_baseline','senti_by_refined_snownlp','senti_by_jieba_sentiment'],axis=1,inplace=True)\n","df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Index</th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>manually_label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10289</td>\n","      <td>2670985301</td>\n","      <td>2020-02-08 23:52</td>\n","      <td>抗击疫情 我们誓死不退（祝洛东）  #武汉加油##抗击新型肺炎我们在行动##抗疫行动##万众...</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>33252</td>\n","      <td>5622277724</td>\n","      <td>2020-03-06 23:22</td>\n","      <td>#甘肃新增17例境外输入新冠肺炎# 又来[费解]晕死真的是</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>15869</td>\n","      <td>5936449524</td>\n","      <td>2020-02-15 23:54</td>\n","      <td>#新冠肺炎已开始在日本流行# 我靠</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>14422</td>\n","      <td>2211096821</td>\n","      <td>2020-02-13 23:59</td>\n","      <td>日本再确诊1例新冠肺炎病例 总确诊数已达251例 http://t.cn/A6hGfqoZ</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23309</td>\n","      <td>6303064815</td>\n","      <td>2020-02-23 23:59</td>\n","      <td>//@微博辟谣:#微博辟谣# 金银潭医院：军运会五外籍运动员患的都是疟疾，与新冠肺炎无关</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Index     user_id        created_at  ... repost_num  comment_num  manually_label\n","0  10289  2670985301  2020-02-08 23:52  ...          0            0               1\n","1  33252  5622277724  2020-03-06 23:22  ...          0            1               0\n","2  15869  5936449524  2020-02-15 23:54  ...          0            0               0\n","3  14422  2211096821  2020-02-13 23:59  ...          0            0               0\n","4  23309  6303064815  2020-02-23 23:59  ...          0            0               0\n","\n","[5 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"mpsCc6eb8cWI","colab_type":"code","colab":{}},"source":["content_list = []\n","for comm in df.content.tolist():\n","    text = re.sub(r'(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)','',comm)\n","    text = re.sub(r'(?:回复)?(?://)?@[\\w\\u2E80-\\u9FFF]+:?|\\[\\w+\\]', ',',text)\n","    r='[’！？：；【】，、《》!\"#$%&\\'()（）“”…*+,-./:;<=>?@[\\\\]^_`{|}~]+'\n","    text = re.sub(r, '', text)\n","    content_list.append(text)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LvgjJfqg8cWO","colab_type":"code","colab":{}},"source":["df['content'] = content_list"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7BAy23hd8cWS","colab_type":"code","outputId":"81c91acf-862a-4339-a0da-6fc107d4a423","executionInfo":{"status":"ok","timestamp":1587362008088,"user_tz":-480,"elapsed":3234,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["import jieba\n","df['content'] = df['content'].apply(lambda i:jieba.cut(i) )\n","df['content'] =[' '.join(i) for i in df['content']]\n","df.head()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Building prefix dict from the default dictionary ...\n","Dumping model to file cache /tmp/jieba.cache\n","Loading model cost 0.979 seconds.\n","Prefix dict has been built successfully.\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Index</th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>manually_label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10289</td>\n","      <td>2670985301</td>\n","      <td>2020-02-08 23:52</td>\n","      <td>抗击 疫情   我们 誓死 不退 祝 洛东     武汉 加油 抗击 新型 肺炎 我们 在 ...</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>33252</td>\n","      <td>5622277724</td>\n","      <td>2020-03-06 23:22</td>\n","      <td>甘肃 新增 17 例 境外 输入 新冠 肺炎   又 来 晕死 真的 是</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>15869</td>\n","      <td>5936449524</td>\n","      <td>2020-02-15 23:54</td>\n","      <td>新冠 肺炎 已 开始 在 日本 流行   我 靠</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>14422</td>\n","      <td>2211096821</td>\n","      <td>2020-02-13 23:59</td>\n","      <td>日本 再 确诊 1 例新冠 肺炎 病例   总 确诊 数已 达 251 例</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23309</td>\n","      <td>6303064815</td>\n","      <td>2020-02-23 23:59</td>\n","      <td>微博 辟谣   金银 潭 医院 军运会 五 外籍 运动员 患 的 都 是 疟疾 与 新冠 肺...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Index     user_id        created_at  ... repost_num  comment_num  manually_label\n","0  10289  2670985301  2020-02-08 23:52  ...          0            0               1\n","1  33252  5622277724  2020-03-06 23:22  ...          0            1               0\n","2  15869  5936449524  2020-02-15 23:54  ...          0            0               0\n","3  14422  2211096821  2020-02-13 23:59  ...          0            0               0\n","4  23309  6303064815  2020-02-23 23:59  ...          0            0               0\n","\n","[5 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"WCmp7P2q8cWd","colab_type":"code","outputId":"617778c3-dc85-4b2c-b8c3-fb7301438f83","executionInfo":{"status":"ok","timestamp":1587362103840,"user_tz":-480,"elapsed":652,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["y = df.manually_label.values\n","x_train, x_valid, y_train, y_valid = train_test_split(df.content.values, y, stratify=y, random_state=42, test_size=0.2, shuffle=True)\n","print (x_train.shape)\n","print (x_valid.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(2400,)\n","(600,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kjNG0_Pl8cWg","colab_type":"code","colab":{}},"source":["def multiclass_logloss(actual, predicted, eps=1e-15):\n","    \"\"\"对数损失度量（Logarithmic Loss  Metric）的多分类版本。\n","    :param actual: 包含actual target classes的数组\n","    :param predicted: 分类预测结果矩阵, 每个类别都有一个概率\n","    \"\"\"\n","    # Convert 'actual' to a binary array if it's not already:\n","    if len(actual.shape) == 1:\n","        actual2 = np.zeros((actual.shape[0], predicted.shape[1]))\n","        for i, val in enumerate(actual):\n","            actual2[i, val] = 1\n","        actual = actual2\n","\n","    clip = np.clip(predicted, eps, 1 - eps)\n","    rows = actual.shape[0]\n","    vsota = np.sum(actual * np.log(clip))\n","    return -1.0 / rows * vsota"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Q8SUe0KD8cWj","colab_type":"code","colab":{}},"source":["def number_normalizer(tokens):\n","    \"\"\" 将所有数字标记映射为一个占位符（Placeholder）。\n","    对于许多实际应用场景来说，以数字开头的tokens不是很有用，\n","    但这样tokens的存在也有一定相关性。 通过将所有数字都表示成同一个符号，可以达到降维的目的。\n","    \"\"\"\n","    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n","\n","\n","class NumberNormalizingVectorizer(TfidfVectorizer):\n","    def build_tokenizer(self):\n","        tokenize = super(NumberNormalizingVectorizer, self).build_tokenizer()\n","        return lambda doc: list(number_normalizer(tokenize(doc)))\n","\n","stwlist=[line.strip() for line in open('cn_stopwords.txt',\n","'r',encoding='utf-8').readlines()]\n","tfv = NumberNormalizingVectorizer(min_df=3,  \n","                                  max_df=0.5,\n","                                  max_features=None,                 \n","                                  ngram_range=(1,2), \n","                                  use_idf=True,\n","                                  smooth_idf=True,\n","                                  stop_words = stwlist)\n","\n","tfv.fit(list(x_train) + list(x_valid))\n","x_train_tfv =  tfv.transform(x_train) \n","x_valid_tfv = tfv.transform(x_valid)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MkxNwg8J8cWl","colab_type":"text"},"source":["#### Bayes"]},{"cell_type":"code","metadata":{"id":"2ychumWN8cWl","colab_type":"code","outputId":"f8008203-62e8-40e1-927f-bf84c0b81248","executionInfo":{"status":"ok","timestamp":1587359434108,"user_tz":-480,"elapsed":775,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":212}},"source":["clf = MultinomialNB()\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.765 \n","              precision    recall  f1-score   support\n","\n","          -1       0.72      0.43      0.54       161\n","           0       0.62      0.85      0.72       258\n","           1       0.73      0.60      0.66       181\n","\n","    accuracy                           0.66       600\n","   macro avg       0.69      0.63      0.64       600\n","weighted avg       0.68      0.66      0.65       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"59RDeIdQ8cWu","colab_type":"text"},"source":["#### Logistic Regression"]},{"cell_type":"code","metadata":{"id":"gT-et_PA8cWv","colab_type":"code","outputId":"a1cde2d3-e0c8-4fb4-a310-16d6a1742752","executionInfo":{"status":"ok","timestamp":1587359437484,"user_tz":-480,"elapsed":1105,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":212}},"source":["clf = LogisticRegression(C=1.0,solver='lbfgs',multi_class='multinomial')\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.633 \n","              precision    recall  f1-score   support\n","\n","          -1       0.64      0.53      0.58       161\n","           0       0.64      0.79      0.71       258\n","           1       0.76      0.61      0.68       181\n","\n","    accuracy                           0.67       600\n","   macro avg       0.68      0.65      0.66       600\n","weighted avg       0.68      0.67      0.67       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yzGSF39j8cWz","colab_type":"text"},"source":["#### SVM"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"G5oH8MhR8cW0","colab_type":"code","outputId":"54bc886f-a80c-49a9-b77f-bb1e8004b261","executionInfo":{"status":"ok","timestamp":1587359456285,"user_tz":-480,"elapsed":12160,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["%%time\n","clf = SVC(C=1.0,kernel='rbf',probability=True)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.918 \n","              precision    recall  f1-score   support\n","\n","          -1       0.60      0.47      0.52       161\n","           0       0.60      0.79      0.68       258\n","           1       0.74      0.56      0.64       181\n","\n","    accuracy                           0.63       600\n","   macro avg       0.65      0.61      0.62       600\n","weighted avg       0.64      0.63      0.63       600\n","\n","CPU times: user 11.2 s, sys: 739 µs, total: 11.2 s\n","Wall time: 11.3 s\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"xsLnXbMQ8cW4","colab_type":"text"},"source":["#### XGBoost"]},{"cell_type":"code","metadata":{"id":"8YF3BpVj8cW5","colab_type":"code","outputId":"bd432168-9954-448e-c0ec-a55fb7913437","executionInfo":{"status":"ok","timestamp":1587362201933,"user_tz":-480,"elapsed":20214,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, \n","                        subsample=0.8, nthread=10, learning_rate=0.1)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 2.094 \n","              precision    recall  f1-score   support\n","\n","          -1       0.61      0.52      0.56       161\n","           0       0.64      0.77      0.70       258\n","           1       0.73      0.61      0.66       181\n","\n","    accuracy                           0.66       600\n","   macro avg       0.66      0.63      0.64       600\n","weighted avg       0.66      0.66      0.65       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gcj8rHhSIxVp","colab_type":"text"},"source":["Bagging Classifier on Logistic Regression"]},{"cell_type":"code","metadata":{"id":"72EwGqyq8cW9","colab_type":"code","outputId":"68f48412-9024-4ff5-9493-52afd4d73d42","executionInfo":{"status":"ok","timestamp":1587367396785,"user_tz":-480,"elapsed":5180,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":212}},"source":["from sklearn.ensemble import BaggingClassifier\n","clf = BaggingClassifier(base_estimator=LogisticRegression(C=1.0,solver='lbfgs',multi_class='multinomial'),n_estimators=10, random_state=0)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.599 \n","              precision    recall  f1-score   support\n","\n","          -1       0.66      0.52      0.58       161\n","           0       0.63      0.81      0.71       258\n","           1       0.77      0.60      0.67       181\n","\n","    accuracy                           0.67       600\n","   macro avg       0.69      0.64      0.66       600\n","weighted avg       0.68      0.67      0.66       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ryzssiHfMKPG","colab_type":"text"},"source":["Random Forest"]},{"cell_type":"code","metadata":{"id":"EehtJC-e8cXA","colab_type":"code","outputId":"99e8441d-6f7f-4f58-902e-11098740b35a","executionInfo":{"status":"ok","timestamp":1587362776458,"user_tz":-480,"elapsed":605,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["from sklearn.ensemble import RandomForestClassifier\n","clf = RandomForestClassifier(max_depth=2, random_state=0)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.156 \n","              precision    recall  f1-score   support\n","\n","          -1       1.00      0.01      0.01       161\n","           0       0.43      1.00      0.60       258\n","           1       1.00      0.02      0.03       181\n","\n","    accuracy                           0.44       600\n","   macro avg       0.81      0.34      0.22       600\n","weighted avg       0.76      0.44      0.27       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Q3lt0sQyTWgw","colab_type":"text"},"source":["Adaboost"]},{"cell_type":"code","metadata":{"id":"NjbsiANl8cXF","colab_type":"code","outputId":"2e6a9a5f-d58b-4cca-8f5a-68b8231b63bc","executionInfo":{"status":"ok","timestamp":1587364713989,"user_tz":-480,"elapsed":1798,"user":{"displayName":"Phoebe Feng","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIB5Y1zNlVR4dHnP1nG07nrhQ_7fkEmS2q7cIq=s64","userId":"12835721552316029415"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["from sklearn.ensemble import AdaBoostClassifier\n","clf = AdaBoostClassifier(n_estimators=100, random_state=0)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 1.109 \n","              precision    recall  f1-score   support\n","\n","          -1       0.53      0.47      0.50       161\n","           0       0.60      0.72      0.65       258\n","           1       0.69      0.56      0.62       181\n","\n","    accuracy                           0.60       600\n","   macro avg       0.61      0.58      0.59       600\n","weighted avg       0.61      0.60      0.60       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GlCzY_7QUOae","colab_type":"text"},"source":["#### LightGBM"]},{"cell_type":"code","metadata":{"id":"6Gcq7KPO8cXH","colab_type":"code","outputId":"5a1c3de4-fea4-42c2-a133-e24b70662619","executionInfo":{"status":"ok","timestamp":1587364952356,"user_tz":-480,"elapsed":12120,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":212}},"source":["import lightgbm as lgb\n","clf = lgb.LGBMClassifier(max_depth=7, n_estimators=500, colsample_bytree=0.8, \n","                        subsample=0.8, nthread=10, learning_rate=0.1)\n","clf.fit(x_train_tfv, y_train)\n","y_pred_proba = clf.predict_proba(x_valid_tfv)\n","y_pred = clf.predict(x_valid_tfv)\n","print (\"logloss: %0.3f \" % multiclass_logloss(y_valid, y_pred_proba))\n","print(classification_report(y_valid, y_pred,target_names = ['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["logloss: 2.724 \n","              precision    recall  f1-score   support\n","\n","          -1       0.59      0.54      0.56       161\n","           0       0.64      0.73      0.68       258\n","           1       0.70      0.61      0.65       181\n","\n","    accuracy                           0.64       600\n","   macro avg       0.64      0.63      0.63       600\n","weighted avg       0.64      0.64      0.64       600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"54jXhUJdVl9X","colab_type":"text"},"source":["#### Prediction for the whole dataset"]},{"cell_type":"code","metadata":{"id":"AJkreTYqVlE3","colab_type":"code","colab":{}},"source":["df_all = pd.read_csv('df_final.csv',encoding='utf-8')\n","df_all = df_all[(df_all.content != '转发微博')&(df_all.content != 'repost')&(df_all.content != '')]\n","df_all = df_all.dropna()\n","df_all = df_all.reset_index(drop=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QkSAdBeyVlHh","colab_type":"code","outputId":"0feb5ba9-88dc-4570-816d-b8b191d0f855","executionInfo":{"status":"ok","timestamp":1587365379132,"user_tz":-480,"elapsed":1036,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":411}},"source":["df_all"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1681872675</td>\n","      <td>2020-01-20 08:10</td>\n","      <td>发现满屏都是关于新冠肺炎在全国各地传播的新闻，虽可控可防，但还是觉得可怕，不禁想起09年非典...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1306052985</td>\n","      <td>2020-01-20 08:21</td>\n","      <td>三条通报的时间点好一致//首发症状大多为发热、咳嗽、胸闷、呼吸困难，这新冠肺炎还挺厉害的……</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2218332387</td>\n","      <td>2020-01-20 09:08</td>\n","      <td>关于最近很猖狂的新冠型肺炎病毒 我们紧急召开了第二届家庭会议 与会人员：我、谢胖子 会议主题...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2167644005</td>\n","      <td>2020-01-20 13:49</td>\n","      <td>有过上海世博会的经历，各种排队不是个事儿。有过非典型肺炎的经验，武汉新冠也不会恐慌。</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7339096067</td>\n","      <td>2020-01-20 18:34</td>\n","      <td>#星座[超话]# 仔细想想，自打1.12土冥合相之后就没什么好消息啊…叹气 更不用说这两天突...</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36650</th>\n","      <td>1804649801</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>#西班牙新冠肺炎确诊病例已达999例# 愿平安！🙏🙏</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36651</th>\n","      <td>5344250889</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>【巩固拓展决胜态势，坚决打赢两场硬仗！徐衣显在这场专题新闻发布会上透漏了……】昨日下午，河南...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36652</th>\n","      <td>5094666244</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>#钟南山院士公布研究最新成果# 【钟南山院士：已开发快速检测试纸、恒温扩增芯片两个新方法 助...</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36653</th>\n","      <td>5109213891</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>【李克强主持召开中央应对新冠肺炎疫情工作领导小组会议】3月9日，中共中央政治局常委、国务院总...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36654</th>\n","      <td>1931744185</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>《美媒：和特朗普握过手的人刚刚确认接触过确诊病例……》特朗普2月29日曾被拍到与美国保守联盟...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>36655 rows × 6 columns</p>\n","</div>"],"text/plain":["          user_id        created_at  ... repost_num  comment_num\n","0      1681872675  2020-01-20 08:10  ...          0            0\n","1      1306052985  2020-01-20 08:21  ...          0            0\n","2      2218332387  2020-01-20 09:08  ...          1            3\n","3      2167644005  2020-01-20 13:49  ...          0            0\n","4      7339096067  2020-01-20 18:34  ...          0            7\n","...           ...               ...  ...        ...          ...\n","36650  1804649801  2020-03-09 23:59  ...          0            0\n","36651  5344250889  2020-03-09 23:59  ...          0            0\n","36652  5094666244  2020-03-09 23:59  ...          2            0\n","36653  5109213891  2020-03-09 23:59  ...          0            0\n","36654  1931744185  2020-03-09 23:59  ...          0            0\n","\n","[36655 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"code","metadata":{"id":"3GPzdqrkWeOX","colab_type":"code","outputId":"e87c94b2-58cc-4bc5-a765-1b97a27d90bb","executionInfo":{"status":"ok","timestamp":1587365688585,"user_tz":-480,"elapsed":847,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":230}},"source":["df.set_index('Index',inplace=True)\n","df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>manually_label</th>\n","    </tr>\n","    <tr>\n","      <th>Index</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10289</th>\n","      <td>2670985301</td>\n","      <td>2020-02-08 23:52</td>\n","      <td>抗击 疫情   我们 誓死 不退 祝 洛东     武汉 加油 抗击 新型 肺炎 我们 在 ...</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>33252</th>\n","      <td>5622277724</td>\n","      <td>2020-03-06 23:22</td>\n","      <td>甘肃 新增 17 例 境外 输入 新冠 肺炎   又 来 晕死 真的 是</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15869</th>\n","      <td>5936449524</td>\n","      <td>2020-02-15 23:54</td>\n","      <td>新冠 肺炎 已 开始 在 日本 流行   我 靠</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14422</th>\n","      <td>2211096821</td>\n","      <td>2020-02-13 23:59</td>\n","      <td>日本 再 确诊 1 例新冠 肺炎 病例   总 确诊 数已 达 251 例</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23309</th>\n","      <td>6303064815</td>\n","      <td>2020-02-23 23:59</td>\n","      <td>微博 辟谣   金银 潭 医院 军运会 五 外籍 运动员 患 的 都 是 疟疾 与 新冠 肺...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          user_id        created_at  ... comment_num  manually_label\n","Index                                ...                            \n","10289  2670985301  2020-02-08 23:52  ...           0               1\n","33252  5622277724  2020-03-06 23:22  ...           1               0\n","15869  5936449524  2020-02-15 23:54  ...           0               0\n","14422  2211096821  2020-02-13 23:59  ...           0               0\n","23309  6303064815  2020-02-23 23:59  ...           0               0\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"qeRv51IHXoSd","colab_type":"code","outputId":"ae5909fc-383c-4f29-8c9d-1d5651a2dffb","executionInfo":{"status":"ok","timestamp":1587365808548,"user_tz":-480,"elapsed":805,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":411}},"source":["df_train = df\n","df_pred = df_all.drop(df_train.index)\n","df_pred"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1681872675</td>\n","      <td>2020-01-20 08:10</td>\n","      <td>发现满屏都是关于新冠肺炎在全国各地传播的新闻，虽可控可防，但还是觉得可怕，不禁想起09年非典...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1306052985</td>\n","      <td>2020-01-20 08:21</td>\n","      <td>三条通报的时间点好一致//首发症状大多为发热、咳嗽、胸闷、呼吸困难，这新冠肺炎还挺厉害的……</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2218332387</td>\n","      <td>2020-01-20 09:08</td>\n","      <td>关于最近很猖狂的新冠型肺炎病毒 我们紧急召开了第二届家庭会议 与会人员：我、谢胖子 会议主题...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2167644005</td>\n","      <td>2020-01-20 13:49</td>\n","      <td>有过上海世博会的经历，各种排队不是个事儿。有过非典型肺炎的经验，武汉新冠也不会恐慌。</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7339096067</td>\n","      <td>2020-01-20 18:34</td>\n","      <td>#星座[超话]# 仔细想想，自打1.12土冥合相之后就没什么好消息啊…叹气 更不用说这两天突...</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36650</th>\n","      <td>1804649801</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>#西班牙新冠肺炎确诊病例已达999例# 愿平安！🙏🙏</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36651</th>\n","      <td>5344250889</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>【巩固拓展决胜态势，坚决打赢两场硬仗！徐衣显在这场专题新闻发布会上透漏了……】昨日下午，河南...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36652</th>\n","      <td>5094666244</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>#钟南山院士公布研究最新成果# 【钟南山院士：已开发快速检测试纸、恒温扩增芯片两个新方法 助...</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36653</th>\n","      <td>5109213891</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>【李克强主持召开中央应对新冠肺炎疫情工作领导小组会议】3月9日，中共中央政治局常委、国务院总...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36654</th>\n","      <td>1931744185</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>《美媒：和特朗普握过手的人刚刚确认接触过确诊病例……》特朗普2月29日曾被拍到与美国保守联盟...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>33655 rows × 6 columns</p>\n","</div>"],"text/plain":["          user_id        created_at  ... repost_num  comment_num\n","0      1681872675  2020-01-20 08:10  ...          0            0\n","1      1306052985  2020-01-20 08:21  ...          0            0\n","2      2218332387  2020-01-20 09:08  ...          1            3\n","3      2167644005  2020-01-20 13:49  ...          0            0\n","4      7339096067  2020-01-20 18:34  ...          0            7\n","...           ...               ...  ...        ...          ...\n","36650  1804649801  2020-03-09 23:59  ...          0            0\n","36651  5344250889  2020-03-09 23:59  ...          0            0\n","36652  5094666244  2020-03-09 23:59  ...          2            0\n","36653  5109213891  2020-03-09 23:59  ...          0            0\n","36654  1931744185  2020-03-09 23:59  ...          0            0\n","\n","[33655 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"cxzB0PAIYDWA","colab_type":"code","colab":{}},"source":["def df_preprocessing(df):\n","  content_list = []\n","  for comm in df.content.tolist():\n","    text = re.sub(r'(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)','',comm)\n","    text = re.sub(r'(?:回复)?(?://)?@[\\w\\u2E80-\\u9FFF]+:?|\\[\\w+\\]', ',',text)\n","    r='[’！？：；【】，、《》!\"#$%&\\'()（）“”…*+,-./:;<=>?@[\\\\]^_`{|}~]+'\n","    text = re.sub(r, '', text)\n","    content_list.append(text)\n","  df['content'] = content_list\n","  df['content'] = df['content'].apply(lambda i:jieba.cut(i))\n","  df['content'] = [' '.join(i) for i in df['content']]\n","  return df"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oxGaobjlauq0","colab_type":"code","colab":{}},"source":["df_train = df_preprocessing(df_train)\n","df_pred = df_preprocessing(df_pred)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sh_D9aF-a4Tj","colab_type":"code","outputId":"11171abe-b401-4be8-f01f-35de2ea7b7b7","executionInfo":{"status":"ok","timestamp":1587366579876,"user_tz":-480,"elapsed":841,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":230}},"source":["df_train.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>manually_label</th>\n","    </tr>\n","    <tr>\n","      <th>Index</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10289</th>\n","      <td>2670985301</td>\n","      <td>2020-02-08 23:52</td>\n","      <td>抗击   疫情       我们   誓死   不 退   祝   洛东          ...</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>33252</th>\n","      <td>5622277724</td>\n","      <td>2020-03-06 23:22</td>\n","      <td>甘肃   新增   17   例   境外   输入   新冠   肺炎       又  ...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15869</th>\n","      <td>5936449524</td>\n","      <td>2020-02-15 23:54</td>\n","      <td>新冠   肺炎   已   开始   在   日本   流行       我   靠</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14422</th>\n","      <td>2211096821</td>\n","      <td>2020-02-13 23:59</td>\n","      <td>日本   再   确诊   1   例新冠   肺炎   病例       总   确诊  ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23309</th>\n","      <td>6303064815</td>\n","      <td>2020-02-23 23:59</td>\n","      <td>微博   辟谣       金银   潭   医院   军运会   五   外籍   运动员...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          user_id        created_at  ... comment_num  manually_label\n","Index                                ...                            \n","10289  2670985301  2020-02-08 23:52  ...           0               1\n","33252  5622277724  2020-03-06 23:22  ...           1               0\n","15869  5936449524  2020-02-15 23:54  ...           0               0\n","14422  2211096821  2020-02-13 23:59  ...           0               0\n","23309  6303064815  2020-02-23 23:59  ...           0               0\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"zEAe1muRa55H","colab_type":"code","outputId":"5cf35db0-a046-43dc-812c-a61b36fea61c","executionInfo":{"status":"ok","timestamp":1587366582291,"user_tz":-480,"elapsed":921,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":200}},"source":["df_pred.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1681872675</td>\n","      <td>2020-01-20 08:10</td>\n","      <td>发现 满屏 都 是 关于 新冠 肺炎 在 全国 各地 传播 的 新闻 虽 可控 可防 但 还...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1306052985</td>\n","      <td>2020-01-20 08:21</td>\n","      <td>三条 通报 的 时间 点好 一致 首发 症状 大多 为 发热 咳嗽 胸闷 呼吸困难 这新冠 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2218332387</td>\n","      <td>2020-01-20 09:08</td>\n","      <td>关于 最近 很 猖狂 的 新冠型 肺炎 病毒   我们 紧急召开 了 第二届 家庭 会议  ...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2167644005</td>\n","      <td>2020-01-20 13:49</td>\n","      <td>有过 上海 世博会 的 经历 各种 排队 不是 个 事儿 。 有过 非典型 肺炎 的 经验 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7339096067</td>\n","      <td>2020-01-20 18:34</td>\n","      <td>星座   仔细 想想 自打 112 土冥合 相 之后 就 没什么 好消息 啊 叹气   更 ...</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      user_id        created_at  ... repost_num  comment_num\n","0  1681872675  2020-01-20 08:10  ...          0            0\n","1  1306052985  2020-01-20 08:21  ...          0            0\n","2  2218332387  2020-01-20 09:08  ...          1            3\n","3  2167644005  2020-01-20 13:49  ...          0            0\n","4  7339096067  2020-01-20 18:34  ...          0            7\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"Adei_v92bAT8","colab_type":"code","colab":{}},"source":["x_df_train = df_train.content.values\n","y_df_train = df_train.manually_label.values\n","x_df_pred = df_pred.content.values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QbcjFKw8dSPb","colab_type":"code","colab":{}},"source":["tfv.fit(list(x_df_train) + list(x_df_pred))\n","x_df_train_tfv =  tfv.transform(x_df_train) \n","x_df_pred_tfv = tfv.transform(x_df_pred)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"EPwhqkGPdpVn","colab_type":"code","outputId":"749c2951-e5bc-42db-da91-0290216f2756","executionInfo":{"status":"ok","timestamp":1587367844077,"user_tz":-480,"elapsed":32424,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":266}},"source":["clf = BaggingClassifier(base_estimator=LogisticRegression(C=1.0,solver='lbfgs',multi_class='multinomial'),n_estimators=10, random_state=0)\n","clf.fit(x_df_train_tfv, y_df_train)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BaggingClassifier(base_estimator=LogisticRegression(C=1.0, class_weight=None,\n","                                                    dual=False,\n","                                                    fit_intercept=True,\n","                                                    intercept_scaling=1,\n","                                                    l1_ratio=None, max_iter=100,\n","                                                    multi_class='multinomial',\n","                                                    n_jobs=None, penalty='l2',\n","                                                    random_state=None,\n","                                                    solver='lbfgs', tol=0.0001,\n","                                                    verbose=0,\n","                                                    warm_start=False),\n","                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n","                  max_samples=1.0, n_estimators=10, n_jobs=None,\n","                  oob_score=False, random_state=0, verbose=0, warm_start=False)"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"4tV6lAmpf4yI","colab_type":"code","colab":{}},"source":["y_pred_df_train = clf.predict(x_df_train_tfv)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"miF6Dt1FfvuS","colab_type":"code","outputId":"766cf8e3-7b12-4824-f293-5b128d9cbfb7","executionInfo":{"status":"ok","timestamp":1587367914664,"user_tz":-480,"elapsed":559,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":195}},"source":["print(classification_report(y_pred_df_train,y_df_train,target_names=['-1','0','1']))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","          -1       0.83      0.93      0.88       715\n","           0       0.96      0.86      0.91      1439\n","           1       0.88      0.94      0.91       846\n","\n","    accuracy                           0.90      3000\n","   macro avg       0.89      0.91      0.90      3000\n","weighted avg       0.91      0.90      0.90      3000\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QMduUBZee_7w","colab_type":"code","colab":{}},"source":["y_pred = clf.predict(x_df_pred_tfv)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VvAbiUQ6hDPh","colab_type":"code","outputId":"b7564e4a-8ee9-49a8-c69e-23885f7e5dc5","executionInfo":{"status":"ok","timestamp":1587368396979,"user_tz":-480,"elapsed":813,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":318}},"source":["import seaborn as sns\n","sns.countplot(y_pred)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fa469c69160>"]},"metadata":{"tags":[]},"execution_count":54},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"-Bw3pzeKgeYe","colab_type":"text"},"source":["#### Get the final results"]},{"cell_type":"code","metadata":{"id":"xzfLTJXggdN3","colab_type":"code","outputId":"bf9258d5-e329-4c23-f208-e0735d5d6ed8","executionInfo":{"status":"ok","timestamp":1587368462797,"user_tz":-480,"elapsed":835,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":411}},"source":["df_pred['sentiment'] = y_pred\n","df_pred"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1681872675</td>\n","      <td>2020-01-20 08:10</td>\n","      <td>发现 满屏 都 是 关于 新冠 肺炎 在 全国 各地 传播 的 新闻 虽 可控 可防 但 还...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1306052985</td>\n","      <td>2020-01-20 08:21</td>\n","      <td>三条 通报 的 时间 点好 一致 首发 症状 大多 为 发热 咳嗽 胸闷 呼吸困难 这新冠 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2218332387</td>\n","      <td>2020-01-20 09:08</td>\n","      <td>关于 最近 很 猖狂 的 新冠型 肺炎 病毒   我们 紧急召开 了 第二届 家庭 会议  ...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2167644005</td>\n","      <td>2020-01-20 13:49</td>\n","      <td>有过 上海 世博会 的 经历 各种 排队 不是 个 事儿 。 有过 非典型 肺炎 的 经验 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7339096067</td>\n","      <td>2020-01-20 18:34</td>\n","      <td>星座   仔细 想想 自打 112 土冥合 相 之后 就 没什么 好消息 啊 叹气   更 ...</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36650</th>\n","      <td>1804649801</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>西班牙 新冠 肺炎 确诊 病例 已达 999 例   愿 平安 🙏 🙏</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36651</th>\n","      <td>5344250889</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>巩固 拓展 决胜 态势 坚决 打赢 两场 硬仗 徐衣显 在 这场 专题 新闻 发布会 上 透...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36652</th>\n","      <td>5094666244</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>钟南山 院士 公布 研究 最新 成果   钟南山 院士 已 开发 快速 检测 试纸 恒温 扩...</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36653</th>\n","      <td>5109213891</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>李克强 主持 召开 中央 应对 新冠 肺炎 疫情 工作 领导 小组会议 3 月 9 日 中共...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36654</th>\n","      <td>1931744185</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>美媒 和 特朗普 握 过手 的 人 刚刚 确认 接触 过 确诊 病例 特朗普 2 月 29 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>33655 rows × 7 columns</p>\n","</div>"],"text/plain":["          user_id        created_at  ... comment_num  sentiment\n","0      1681872675  2020-01-20 08:10  ...           0         -1\n","1      1306052985  2020-01-20 08:21  ...           0          0\n","2      2218332387  2020-01-20 09:08  ...           3          0\n","3      2167644005  2020-01-20 13:49  ...           0          0\n","4      7339096067  2020-01-20 18:34  ...           7          0\n","...           ...               ...  ...         ...        ...\n","36650  1804649801  2020-03-09 23:59  ...           0          0\n","36651  5344250889  2020-03-09 23:59  ...           0          0\n","36652  5094666244  2020-03-09 23:59  ...           0          0\n","36653  5109213891  2020-03-09 23:59  ...           0          0\n","36654  1931744185  2020-03-09 23:59  ...           0          0\n","\n","[33655 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"code","metadata":{"id":"xCpvQ7qPizx5","colab_type":"code","colab":{}},"source":["df_train.rename(columns={'manually_label':'sentiment'},inplace=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bi2lFNNNj2ML","colab_type":"code","colab":{}},"source":["df_final_result = pd.concat([df_train,df_pred])\n","df_final_result = df_final_result.sort_index()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sLN37IHSkS0l","colab_type":"code","outputId":"b5b23f85-9343-4b74-c995-648595bbf268","executionInfo":{"status":"ok","timestamp":1587369129547,"user_tz":-480,"elapsed":1040,"user":{"displayName":"吴双","photoUrl":"","userId":"17319169555672259009"}},"colab":{"base_uri":"https://localhost:8080/","height":411}},"source":["df_final_result"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>created_at</th>\n","      <th>content</th>\n","      <th>like_num</th>\n","      <th>repost_num</th>\n","      <th>comment_num</th>\n","      <th>sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1681872675</td>\n","      <td>2020-01-20 08:10</td>\n","      <td>发现 满屏 都 是 关于 新冠 肺炎 在 全国 各地 传播 的 新闻 虽 可控 可防 但 还...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1306052985</td>\n","      <td>2020-01-20 08:21</td>\n","      <td>三条 通报 的 时间 点好 一致 首发 症状 大多 为 发热 咳嗽 胸闷 呼吸困难 这新冠 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2218332387</td>\n","      <td>2020-01-20 09:08</td>\n","      <td>关于 最近 很 猖狂 的 新冠型 肺炎 病毒   我们 紧急召开 了 第二届 家庭 会议  ...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2167644005</td>\n","      <td>2020-01-20 13:49</td>\n","      <td>有过 上海 世博会 的 经历 各种 排队 不是 个 事儿 。 有过 非典型 肺炎 的 经验 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7339096067</td>\n","      <td>2020-01-20 18:34</td>\n","      <td>星座   仔细 想想 自打 112 土冥合 相 之后 就 没什么 好消息 啊 叹气   更 ...</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36650</th>\n","      <td>1804649801</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>西班牙 新冠 肺炎 确诊 病例 已达 999 例   愿 平安 🙏 🙏</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36651</th>\n","      <td>5344250889</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>巩固 拓展 决胜 态势 坚决 打赢 两场 硬仗 徐衣显 在 这场 专题 新闻 发布会 上 透...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36652</th>\n","      <td>5094666244</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>钟南山 院士 公布 研究 最新 成果   钟南山 院士 已 开发 快速 检测 试纸 恒温 扩...</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36653</th>\n","      <td>5109213891</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>李克强 主持 召开 中央 应对 新冠 肺炎 疫情 工作 领导 小组会议 3 月 9 日 中共...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36654</th>\n","      <td>1931744185</td>\n","      <td>2020-03-09 23:59</td>\n","      <td>美媒 和 特朗普 握 过手 的 人 刚刚 确认 接触 过 确诊 病例 特朗普 2 月 29 ...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>36655 rows × 7 columns</p>\n","</div>"],"text/plain":["          user_id        created_at  ... comment_num  sentiment\n","0      1681872675  2020-01-20 08:10  ...           0         -1\n","1      1306052985  2020-01-20 08:21  ...           0          0\n","2      2218332387  2020-01-20 09:08  ...           3          0\n","3      2167644005  2020-01-20 13:49  ...           0          0\n","4      7339096067  2020-01-20 18:34  ...           7          0\n","...           ...               ...  ...         ...        ...\n","36650  1804649801  2020-03-09 23:59  ...           0          0\n","36651  5344250889  2020-03-09 23:59  ...           0          0\n","36652  5094666244  2020-03-09 23:59  ...           0          0\n","36653  5109213891  2020-03-09 23:59  ...           0          0\n","36654  1931744185  2020-03-09 23:59  ...           0          0\n","\n","[36655 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"s7_5EvK3ksrT","colab_type":"code","colab":{}},"source":["df_final_result.to_csv('final_sentiment_result_new.csv')"],"execution_count":0,"outputs":[]}]}