Sentiment Analysis Process for Product's Customer Reviews Using Ontology-Based Approach

Today, data in a vast number of social networks are abundantly utilized to help consumers make decisions in selecting products. While companies endeavor to analyze and interpret the multitude of customer opinions and sentiments, an accurate assessment becomes problematic. Many research studies encounter semantic conflicts of words or synonymous words, and errors occur within the SentiWordNet algorithm, when assessing both positive and negative words in some sentences. The present study, therefore, aims to solve the above-mentioned problems through DBpedia, and addresses the differences in word meanings, and to create a user interface for retrieving products in the form of keywords, in order to help consumers make decisions in selecting products. The efficiency measurement of sentiment analysis within the present study was 94%.