Sentiment Analysis in E-Commerce Platforms: A Review of Current Techniques and Future Directions

Abstract

Sentiment analysis (SA), also referred to as opinion mining, has become a widely used real-world application of natural language processing in recent times. Its main goal is to identify the hidden emotions behind the plain text. SA is especially useful in e-commerce fields, where comments and reviews often contain a wealth of valuable business information that has great research value. The objective of this study is to examine the techniques used for SA in current e-commerce platforms as well as the future directions for SA in e-commerce. After examining the existing systematic review papers, it was found that there is a lack of a single comprehensive review paper that addresses research questions. The findings of this study can provide researchers in the field of SA with a comprehensive understanding of the current techniques and platforms utilized, as well as provide insights into the future directions. Through the utilization of specific keywords, we have identified 271 papers and have chosen 54 experimental papers for review. Among these, 26 papers (representing 48.%) have exclusively employed machine Learning techniques, while 24 (44.%) have looked into addressing SA through deep learning techniques, and 4 (7.%) have employed a hybrid approach using both machine learning and deep learning techniques. Additionally, our review revealed that Amazon and Twitter emerged as the two most favored data sources among researchers. Looking ahead, promising research avenues to include the development of more universal language models, aspect-based SA, implicit aspect recognition and extraction, sarcasm detection, and fine-grained sentiment analysis.

Keywords

• Sentiment analysis(SA)
• E-commerce
• natural language processing
• machine learning
• deep learning
• opinion mining