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This paper covers the two approaches for sentiment analysis: i) lexicon basedmethod; ii) machine learning method. We describe several techniques toimplement these approaches and discuss how they can be adopted for sentimentclassification of Twitter messages. We present a comparative study of differentlexicon combinations and show that enhancing sentiment lexicons with emoticons,abbreviations and social-media slang expressions increases the accuracy oflexicon-based classification for Twitter. We discuss the importance of featuregeneration and feature selection processes for machine learning sentimentclassification. To quantify the performance of the main sentiment analysismethods over Twitter we run these algorithms on a benchmark Twitter datasetfrom the SemEval-2013 competition, task 2-B. The results show that machinelearning method based on SVM and Naive Bayes classifiers outperforms thelexicon method. We present a new ensemble method that uses a lexicon basedsentiment score as input feature for the machine learning approach. Thecombined method proved to produce more precise classifications. We also showthat employing a cost-sensitive classifier for highly unbalanced datasetsyields an improvement of sentiment classification performance up to 7%.

Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination