This paper briefly reports our system for the SemEval-2013 Task 2: sentiment analysis in Twitter. We first used an SVM classifier with a wide range of features, including bag of word features (unigram, bigram), POS features, stylistic features, readability scores and other statistics of the tweet being analyzed, domain names, abbreviations, emoticons in the Twitter text. Then we investigated the effectiveness of these features. We also used character n-gram language models to address the problem of high lexical variation in Twitter text and combined the two approaches to obtain the final results. Our system is robust and achieves good performance on the Twitter test data as well as the SMS test data.
Han REF in his research combine SVM classifier and character N-gram language models for sentiment analysis on Twitter text.
CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text
*SEM
Computer Science