Aspect Extraction Approach for Sentiment Analysis Using Keywords

Sentiment Analysis deals with consumer reviews available on blogs, discussion forums, E-commerce websites, and App Store. These online about products are also becoming essential for consumers companies as well. Consumers rely these to make their decisions very interested in judge services. a precious source of information requirement engineers. But not satisfied the overall sentiment; they like fine-grained knowledge reviews. Owing this, many researchers have developed approaches aspect-based sentiment analysis. Most existing concentrate explicit aspects analyze sentiment, only few studies capturing implicit aspects. This paper proposes Keywords-Based Aspect Extraction method, which captures both It opinion words classifies each aspect. We applied semantic similarity-based WordNet SentiWordNet lexicon improve aspect extraction. used different collections customer experiment purposes, consisting eight datasets over seven domains. compared our approach other state-of-the-art approaches, including Rule Selection using Greedy Algorithm (RSG), Conditional Random Fields (CRF), Rule-based (RubE), Double Propagation (DP). Our results shown better performance than all approaches.