A boosted SVM based sentiment analysis approach for online opinionated text

The opinionated text available on the Internet and Web 2.0 social media has created ample research opportunities related to mining and analyzing public sentiments. At the same time, the large volume of such data poses severe data processing and sentiment extraction related challenges. Different contemporary solutions based on machine learning, dictionary, statistical, and semantic based approaches have been proposed in literature for sentiment analysis of online user-generated data. Recent research studies have proved that supervised machine learning techniques like Naive Bayes (NB) and Support Vector Machines (SVM) are very effective for sentiment based classification of opinionated text. This paper proposes a hybrid sentiment classification model based on Boosted SVM. The proposed model exploits classification performance of two techniques (Boosting and SVM) applied for the task of sentiment based classification of online reviews. The results on movies and hotel review corpora of 2000 reviews have shown that the proposed approach has succeeded in improving performance of SVM when used as a weak learner for sentiment based classification. Specifically, the results show that SVM ensemble with bagging or boosting significantly outperforms a single SVM in terms of accuracy of sentiment based classification.