Cross Domain Sentiment Analysis Using Different Machine Learning Techniques

Abstract

Sentiment analysis is the field of study that focuses on finding effectively the conduct of subjective text by analyzing people’s opinions, sentiments, evaluations, attitudes and emotions towards entities. The analysis of data and extracting the opinion word from the data is a challenging task especially when it involves reviews from completely different domains. We perform cross domain sentiment analysis on Amazon product reviews (books, dvd, kitchen appliances, electronics) and TripAdvisor hotel reviews, effectively classify the reviews to positive and negative polarities by applying various preprocessing techniques like Tokenization, POS Tagging, Lemmatization and Stemming which can enhance the performance of sentiment analysis in terms of accuracy and time to train the classifier. Various methods proposed for document-level sentiment classification like Naive Bayes, k-Nearest Neighbor, Support Vector Machines and Decision Tree are analysed in this work. Cross domain sentiment classification is useful because many times we might not have training corpus of specific domains for which we need to classify the data and also cross domain is favoured by lower computation cost and time. Despite poor performance in accuracy, the time consumed for sentiment classification when multiple testing datasets of different domains are present is far less in case of cross domain as compared to single domain. This work aims to define methods to overcome the problem of lower accuracy in cross-domain sentiment classification using different techniques and taking the benefit of being a faster method.