Enhancing Sentiment Analysis using Rule Mining and Dual Processing

摘要

Sentiment analysis is a widely researched area in computer science that have been attracting researchers since the past few decades. Shift in polarity is a problem that exists in sentiment classification. Another problem is the difficulty in handling inconsistent sentiment polarity between a phrase and its constituent words. For solving both, we perform dual sentiment analysis using an original dataset and its reversed version. We use a sentiment classification approach for classifying the sentiments of the reviews in both the original and reversed sets. This approach is based on segmenting the sentences and then using the segmentation results to find the polarity of the sentences. Then rules are mined to correctly classify the reviews. When this approach is applied on a product review dataset taken from amazon.com, it shows good precision, recall and fl-score values.