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SentiStrength-SE.py
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SentiStrength-SE.py
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# The file is used to analyze the prediction performance of SentiStrength-SE
# Author: happygirlzt
import pandas as pd
import numpy as np
import re
from sklearn.metrics import classification_report, confusion_matrix
def get_confusion_matrix():
pred=pd.read_csv('so-ss.csv', header=None)
#print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
test_df=pd.read_csv('so-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
results = confusion_matrix(true_df,pred_df,labels=[1,0,-1])
print(results)
#get_confusion_matrix()
def analyze_cr():
# replace 'cr-ss.csv' with your prediction file name
pred=pd.read_csv('cr-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score<0:
label.append(-1)
else:
label.append(0)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('cr-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
report=classification_report(true_df, pred_df, output_dict=True)
df = pd.DataFrame(report).transpose()
df.to_csv('./SentiStrength-SE-cr1.csv')
analyze_cr()
def analyze_app():
pred=pd.read_csv('app-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('app-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
#analyze_app()
def analyze_gh():
pred=pd.read_csv('gh-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('gh-test-se.csv',header=None)
test_df.columns=['sentence','label']
test_df['label']=test_df['label'].replace({'positive':1, 'negative':-1, 'neutral':0})
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
#analyze_gh()
def analyze_jira():
pred=pd.read_csv('jira-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('jira-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
#analyze_jira()
def analyze_api():
pred=pd.read_csv('api-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('api-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
#analyze_api()
def analyze_so():
pred=pd.read_csv('so-ss.csv',header=None)
print(pred.shape)
pred.columns=['res']
res_pd=pred['res']
pos_list=[]
neg_list=[]
for sent in res_pd:
cur_list=re.split(r'\t+', sent.rstrip('\t'))[1:]
#print(cur_list)
new_list=cur_list[0].split()
pos_list.append(int(new_list[0]))
neg_list.append(int(new_list[1]))
total = [p + n for p, n in zip(pos_list, neg_list)]
label=[]
for score in total:
if score>0:
label.append(1)
elif score==0:
label.append(0)
else:
label.append(-1)
pred_df=pd.Series(label,dtype='int32')
#print(pred_df)
# read in true lables
test_df=pd.read_csv('so-test-se.csv',header=None)
test_df.columns=['sentence','label']
true_df=pd.Series(test_df['label'],dtype='int32')
print(classification_report(true_df, pred_df))
#analyze_so()