To run the program

Prior to running of the program , extract the following zip files inside electionsentiment folder a. old-data.zip, b. publicsentiments.zip, c. train.zip

1. Crawl tweets :

cd electionsentiment/analysis/twittercrawler

python crawlTweet.py

2. Merge tweeets crawled weekly [ electionsentiment/old-data contains tweets crawled in past weeks, that will be merged]

python mergeTweets.py

The above command will generate the merged files corresponding to Bjp, Congress, both parties , other parties at

electionsentiment/train/raw/LokShobaElc2019... .csv

3. Clean Tweets, process hashtags, url, emojis, retweet_counts and generate train and test datasets

cd electionsentiment/analysis/tweetprocessor

python preprocessTweets.py

4. Run Analytics for party wise comparison charts and polarized tweet classification

cd electionsentiment/algorithms

python sentimentPlots.py

python polarizedTweetPlots.py

5. Run mood prediction algorithms

a. cd electionsentiment/algorithms

b. python fasttextClassify.py

c. python nltkClassify.py

d. python bagging.py

e. python boosting.py

f. python stacking.py

g. python classifygloveattlstm.py [Deep learning models based on Word2Vec Embeddings]

10 python classifyw2veclstm.py [Deep learning LSTM based models]

11 python classifyw2veccnn.py [Deep learning CNN based models]

Links of blogs published in Analytics Vidhya

1. https://medium.com/analytics-vidhya/twitter-sentiment-analysis-for-the-2019-election-8f7d52af1887
2. https://medium.com/analytics-vidhya/sentiment-classification-for-2019-elections-using-text-based-classifiers-217f86b05124
3. https://medium.com/analytics-vidhya/elections-2019-mood-classification-with-text-based-classifiers-ii-bf23c3dfac7f