GitHub - bilalaziz786/Analysis-of-Political-Sentiments-on-Twitter-using-Deep-Learning: Aim was to develop a machine learning model which can analyze sentiments on twitter and to predict the winner of Lok Sabha Elections 2019. Web scraping was used for comments and then applied feature extraction, TF-IDF, Word2Vec, ANN and LSTM to improve the accuracy of model. Softmax function was used for multiclass classification. Learning curve was used to select the epochs and batch size in ANN and LSTM. Word cloud was made to select the most important features in the dataset. Random forest classification was selected as the best classifier with the help of PR curve by using micro average for average precision and recall.

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Aim was to develop a machine learning model which can analyze sentiments on twitter and to predict the winner of Lok Sabha Elections 2019. Web scraping was used for comments and then applied feature extraction, TF-IDF, Word2Vec, ANN and LSTM to improve the accuracy of model. Softmax function was used for multiclass classification. Learning curv…

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
Screenshots		Screenshots
Major (Bilal).pptx		Major (Bilal).pptx
MajorProject.py		MajorProject.py
New_Political_Tweets.csv		New_Political_Tweets.csv
REPORT.pdf		REPORT.pdf
model.bin		model.bin
word2vec.model		word2vec.model