Aspect-based sentiment analysis using grammatical rules, word similarity and SentiCircle

Product reviews can be used as suggestion for companies to improve their services in this digitalization era. These reviews can be presented in more detail using aspect based sentiment analysis. In this research, aspect-based sentiment analysis method is proposed using grammatical rules, word similarity, and SentiCircle. This method began with extracting the candidate aspects rules based on phrase detection in constituency parse. The candidate aspects were categorized using word similarity. Word similarity calculated the similarity value between the candidate aspects and the keywords obtained from Wikipedia. To determine sentiment polarity, SentiCircle is used. SentiCircle can capture the contextual sentiment from the data. The result showed that the proposed method was able to categorize aspects correctly, with the highest f1-measure value of 84%, while sentiment analysis produced the highest f1-measure of 87%.