International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Powered Buyer Sentiment Analysis for E-Commerce |
|---|---|
| ABSTRACT | The exponential growth of e-commerce platforms has amplified the importance of understanding buyer sentiment to enhance customer experience and drive sales. Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and Machine Learning, provides powerful tools for analyzing customer reviews, ratings, and social media interactions. This paper presents a framework for AI-powered buyer sentiment analysis, mainly focus on LSTM, BERT and transformer-based architectures. The study highlights the methodology, experimental setup, and results, demonstrating how sentiment insights can improve recommendation systems, product design, and marketing strategies. |
| AUTHOR | MADHURI DEEKSHITH S, G R CHINMAYI, VIDYA K B Assistant Professor, Department of Computer Science and Design, BIET, Davangere, Karnataka, India U.G. Student, Department of Computer Science and Design, BIET, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312049 |
| pdf/49_AI Powered Buyer Sentiment Analysis for E-Commerce.pdf | |
| KEYWORDS | |
| References | [1] Yue Wng ,“Consumer decision Making in Online Shopping”, 2024,Sentiment analysis applied to e-commerce product. [2] Bing Lilu ,“Sentiment Classification and Decision making ”, 2024, Knowledge and Data Engineering. [3] M.S.S Maheshwari ,“A survey on sentiment Analysis techniques”, M.S.S Maheshwari 2023,International Journal of Computer Applications. [4] Y .Zhang , “Deep Learning Sentiment Analysis.” ,2023 ,Proceedings of the International Conference. [5] P . Gupta, “Aspect of the Comment Analysis of product Reviews”, 2022,Aspect of focusing on product features. [6] A .Agarwal , “Sentiment Analysis of Social Media:A survey”, 2021 ,Challenges of finding the Languages. [7] Krishna Murty, “A feedback Analyzing with e commerce”,2021,Usage of the particular product with Conferences. [8] Nijlingappa, “ Comment Analysis with the e commerce”,2021,challenges of analyzing the product based on the emojies. [9] Gowdara jayadevappa, “Comment fixation based sentiment” ,2020,Comment fixation of the making Analysis. [10] Praphula Kumar Jain & Rajendra Pamula, “A systematic review of the customers,” arXiv preprint, Aug 2022. [11] Kalleshappa, “Sentiment Analysis of Consumer Reviews Analyzer” ,2021. [12] Magesh G & P. Swarnalatha, “Analyzing Customer Sentiments Using Machine Learning Techniques,” 2020. [13] Yadav Pinky & Madhumita Kathuria, “Comment Analysis using Various Machine Learning Techniques: A Review,” IEIE Transactions, 2020. [14] Anandan Chinnalagu & Ashok Kumar Durairaj, “Context-based sentiment analysis,” PeerJ Computer Science, Dec 2020. [15] Rajesh Bose, Sandip Roy & Debabrata Sarddar, “Sentiment Analysis on Online Product Reviews,” ,2017. [16] Jagadeesh Panthati, Jasmine Bhaskar, Tarun Kumar Ranga & Manish Reddy Challa, “Sentiment Analysis of Product ”, 2016 [17] A. Fred Agarap, “Statistical Analysis on E-Commerce Reviews”, arXiv preprint, May 2023. [18] S. Ghatora, S. E. Hosseini, S. Pervez, M. J. Iqbal & N. Shaukat, “Sentiment Analysis of Product Reviews and Pre-Trained LLM,” Big Data and Cognitive Computing, 2024. [19] Adarsh Godia & L. K. Tiwari, “Sentiment Analysis and Classification of Product Reviews: A Comprehensive Study Using NLP”, in 2024 . [20] C. Sharon Roji Priya & P. Deepalakshmi, “Study on Online Review Based Consumer Sentimental Analysis,” in 2014 IEEE World Conference on Applied Intelligence and Computing (AIC). |
