Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach

Abstract

Online social networks have become extremely popular with the ever-increasing reachability of internet to the common person. There are millions of tweets, Facebook messages, and product reviews posted every day. Such huge amount of data presents an opportunity to analyze the sentiment of masses in order to facilitate the decision making for the betterment of society. Sentiment analysis is the research area that quantitates the opinions expressed in natural language. It is a combination of various research fields such as text mining, natural language processing, artificial intelligence, statistics. The application of supervised machine learning algorithms is limited due to the unavailability of labeled data whereas the unsupervised or lexicon-based methodologies show weak performance. This scenario sets the stage for transfer learning or cross-domain learning approaches where the knowledge is learned from the source domain which is then applied to the target domain. The proposed approach computes the feature weights by the application of cosine similarity measure to SentiWordNet and generates revised sentiment scores. Model learning is performed by support vector machine using two experimental settings, i.e., single source and multiple target domains and multiple source and single target domains (MSST). Nine benchmark datasets have been employed for performance evaluation. Best performance was obtained using the MSST settings with 85.05% accuracy, 85.01% precision, 85.10% recall, and 85.05% F-measure. State-of-the-art performance comparison proved that the cosine similarity-based transfer learning approach outperforms other approaches.