The Impact of Features Extraction on the Sentiment Analysis
Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P.
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Twitter sentiment is used to clarify the tweet into neutral, positive, or negative.
SS-Tweet dataset then applied six processing techniques on the dataset and extracted features using N-grams and TF-IDF techniques.
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Applied 5 different algorithm of classification on the SS-Tweet dataset considering two features (TF-IDF and N-Grams)
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Logistic regression is the best algorithm for sentiment analysis and both feature extraction techniques are good enough.
Features like word polarity score features, word embeddings, twitter specific features.