Cross-domain sentiment analysis on social media interactions using senti-lexicon based hybrid features

Analyzing the sentiment information from the social media interactions is a rapidly growing research area. Several studies in the literature focus on modeling the sentiment information using linguistics, generic word counts and even the contextual information, including the presence of punctuations, elongated words, emoticons, etc. In this paper, we experiment on the effectiveness of lexicon information in combination with other information, for the effective analysis of sentiment in social interactions. The objective of this study is to experimentally verify how senti-lexicons can take part in the process of modeling the sentiment information even in cross-domain sentiment analysis. In general, this paper explores the effectiveness of several feature vectors including the generic Bag of Word (BoW), linguistic (N-Gram and Part-of-Speech (POS)) and the lexicon features (number of positive and negative words). Other than the traditional features we generate hybrid features by combining the lexicon features with the BoW and linguistic features. We conduct the experiments on sentiment classification using supervised models like Linear SVC (L-SVC), Multi-Layer Perceptron (MLP), Multinomial Naïve Bayes (MNB) and Decision Tree (DT). The experiments are conducted on three different types of sentiment document datasets - the Amazon food review dataset, student opinion tweet dataset, and the Large Movie Review Dataset v1.0. We also verify the efficacy of these features in cross-domain sentiment analysis. Experiments show that hybridizing the BoW, linguistic N-Gram and POS method with lexicon features improves the accuracy of sentiment classification even for cross-domain sentiment analysis.