Aspect Based Sentiment Analysis in E-Commerce Website
Aarati D. Fuse1, Pradhumnta S. Arbat2, Anjali S. Chaudhary3, Anurag D. Kale4, Ankush.V. Dahat5.
1Student, P.R. Pote Patil College of Engineering Management, Amravati
2Student, P.R. Pote Patil College of Engineering and Management, Amravati
3Student, P.R. Pote Patil College of Engineering and Management, Amravati
4Student, P.R. Pote Patil College of Engineering and Management, Amravati
5Assistant Professor, P.R. Pote Patil College of Engineering and Management, Amravati
ABSTRACT— The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects. Sentiment Analysis is the most commonly used approach to analyze data which is in the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. A wide range of text data is getting generated in the form of suggestions, feedbacks, tweets and comments. E-Commerce portals are generating a lot of data every day in the form of customer reviews.
Analyzing E-Commerce data will help online retailers to understand customer expectations, provide better shopping experience and to increase the sales. Sentiment Analysis can be used to identify positive, negative and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Mostly sentiment Analysis is done using a single machine learning algorithm. This work uses Amazon customer review data and focuses on finding aspect terms from each review, identifying the Parts-of-Speech, applying classification algorithms to find the score of positivity, negativity and neutrality of each review.
Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.
Keywords: Sentiment Analysis (SA), Aspect based sentiment analysis (ABSA), Natural Language Processing(NLP).
