Sentiment Analysis using Feature Extraction and Dictionary-Based Approaches

Natural Language Processing (NLP) is a study of computational treatment of human language in order to make it understandable for computers. It is used in the research fields like artificial intelligence, information engineering, statistics, sentiment analysis and linguistics. Sentiment analysis plays significant role in NLP which performs the series of operations computationally to identify and categorize the sentiment conveyed in segment of words. The proposed approach is to detect the polarity of words from twitter using feature extraction and dictionary-based methods. Method are compared with feature engineering techniques like CountVectorizer, TF-IDF, Word2Vec by using machine learning classifier and also scoring of each word is done by SentiWordNet, VADER dictionaries. From the results, it clarifies that feature extraction outperforms the dictionary-based method. The proposed model gives better accuracy to detect the polarity.