SWIMS: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis

Abstract Sentiment Analysis, also called Opinion Mining, is currently one of the most studied research fields. Its aim is to analyze publics’ sentiments, opinions, attitudes etc., towards different elements such as topics, products, individuals, organizations, or services. Sentiment classification can be achieved by machine learning or lexical based methodologies or a combination of both. In an effort to improve the performance of domain independent lexicons, this research incorporates machine learning with a lexical based approach introducing a new framework called SWIMS to determine the feature weight based on a well-known general-purpose sentiment lexicon, SentiWordNet. Support vector machine is used to learn the feature weights and an intelligent model selection approach is employed in order to enhance the classification performance. The features are selected based on their subjectivity and the effects of feature selection with respect to their part of speech information are studied extensively. Seven benchmark datasets have been used in this research including large movie review dataset, multi-domain sentiment dataset and Cornell movie review dataset, all of which are available online. In-depth performance comparison is conducted with the state of art machine learning approaches and lexical based methodologies. The evaluation of performance measures proves that the proposed framework outperforms other techniques for sentiment analysis.