Twitter Sentiment Analysis with Linear SVM Classifier with TF-IDF and word2vec features.

This project describes how I developed my understanding of text sentiment classification by implementing an SVM classifier and a Logistic Regressor and performing some further experiments to seek approaches that also work. The main system that I tried to implement for the final project is the approach used by NILC-USP that participated in SemEval 2017. NILC-USP uses three classifiers which is trained in a different feature space using bag-of-words model and word embeddings to represent sentences and uses a Linear SVM and a Logistic Regressor as base classifiers. Instead of implementing the voting ensemble, I focused more on how each of three classifiers was built, strengths and drawbacks of the system, and how well it performs.

• Scikit Learn
• Gensim (Word2Vec)
• NLTK