OTAWE-Optimized Topic-Adaptive Word Expansion for Cross Domain Sentiment Classification on Tweets

Abstract

The enormous growth of Internet usage, number of social interactions, and activities in social networking sites results in users adding their opinions on the products. An automated system, called sentiment classifier, is required to extract the sentiments and opinions from social media data. Classifier that is trained using the labeled tweets of one domain may not efficiently classify the tweets from another domain. This is a basic problem with the tweets as twitter data is very diverse. Therefore, Cross Domain Sentiment Classification is required. In this paper, we propose a semi-supervised domain-adaptive sentiment classifier with Optimized Topic-Adaptive Word Expansion (OTAWE) model on tweets. Initially, the classifier is trained on common sentiment words and mixed labeled tweets from various topics. Then, OTAWE algorithm selects more reliable unlabeled tweets from a particular domain and updates domain-adaptive words in every iteration. OTAWE outperforms existing domain-adaptive algorithms as it saves the feature weights after every iteration. This ensures that moderate sentiment words are not missed out and avoids the inclusion of weak sentiment words.