#Project of Sentiment Analysis for the course of Big Data Analysis of the Department of Computer Engineering of Modena and Reggio Emilia.#
##Library used in the project##
-
Tweepy - https://github.com/tweepy/tweepy
-
Natural Language Toolkit - http://nltk.org/install.html
To install additional modules and datasource (Wordnet and other modules are required) launch the following command on a python shell:
import nltk
nltk.download()
Download the followings modules:
- Stopword
- Wordnet
- Wordnet_ic
- All the modules in models
##Project usage##
###Tweet###
The script named ExtractTweet.py can be used to download tweets in a csv file. This script is configurable by this file: config.json
The configurable fields are:
- consumer_key
- consumer_secret
- access_token
- access_token_secret
These fields can be retrieved from https://dev.twitter.com after creating an account and an application
- file_name (name of the
cvsoutput file) - count (number of tweet to download)
- filter (a word used to filter the tweet in output)
The CSV file produced in output can be used as arg of the other three script:
DeriveTweetSentimentEasy.py: This script uses AFINN-111.txt as vocabulary to try to assign a sentiment score to a tweet.NewTermSentimentInference.py: This script try to assign a sentiment score to the words that are not present in AFINN-111.txt based on the sentiment score of a group of tweets.SentiWordnet.py: This script uses SentiWordNet as vocabulary to try to assign a sentiment score to a tweet. The metrics of the scoring and the annotation process are more complex in this script.
###Document Sentiment Classification###
The script is called DocumentSentimentClassification.py and implements a simple method for document sentiment classification.
it possible to set some configuration parameters in the top of Python script:
- FILE_NAME -> name of the file .txt on which you want execute the classification
- API_KEY_BING -> Api Key Bing [http://it.bing.com/dev/en-us/dev-center](Bing Dev)
- API_KEY_GOOGLE -> Api Key for Custom Search Api [https://cloud.google.com/console](Google Api Console)
- USE_GOOGLE -> Enable (True) or Disable (False) the use of the Google Api for Custom Search ( 100 query for day without have to pay )
For an interactive example with ipython, go into the folder BDA_Senti_ipython and launch the command:
$ ipython notebook --pylab inline
###Slides###