Study of Automatic Extraction, Classification, and Ranking of Product Aspects Based on Sentiment Analysis of Reviews

It is very common for a customer to read reviews about the product before making a final decision to buy it. Customers are always eager to get the best and the most objective information about the product they wish to purchase and reviews are the major source to obtain this information. Although reviews are easily accessible from the web, but since most of them carry ambiguous opinion and different structure, it is often very difficult for a customer to filter the information he actually needs. This paper suggests a framework, which provides a single user interface solution to this problem based on sentiment analysis of reviews. First, it extracts all the reviews from different websites carrying varying structure, and gathers information about relevant aspects of that product. Next, it does sentiment analysis around those aspects and gives them sentiment scores. Finally, it ranks all extracted aspects and clusters them into positive and negative class. The final output is a graphical visualization of all positive and negative aspects, which provide the customer easy, comparable, and visual information about the important aspects of the product. The experimental results on five different products carrying 5000 reviews show 78% accuracy. Moreover, the paper also explained the effect of Negation, Valence Shifter, and Diminisher with sentiment lexicon on sentiment analysis, and concluded that they all are independent of the case problem, and have no effect on the accuracy of sentiment analysis.