This paper presents a lightweight, browser-based sentiment analysis tool designed for real-time classification of product reviews. The system employs a lex browser-based icon-driven approach to determine whether a review expresses positive, negative, or neutral sentiment, and provides transparent explanations by highlighting influential words and confidence scores. Unlike conventional sentiment analysis systems that rely on server-side computation or external APIs, the proposed solution operates entirely on the client side, ensuring user privacy, eliminating latency, and requiring no installation or configuration. The tool demonstrates practical applicability in e-commerce environments, educational contexts, and privacy-sensitive applications where rapid, interpretable sentiment assessment is essential.
Keywords
Sentiment Analysis, Product Reviews, Web Application, Natural Language Processing, Text Classification.
Conclusion
This research introduces a lightweight, privacy-preserving sentiment analysis tool that operates entirely on the client side. While lexicon-based methods cannot match the accuracy of deep learning models, they offer significant advantages in speed, transparency, and ease of deployment. The tool is suitable for rapid sentiment assessment, educational use, and privacy-sensitive environments.
References
[1] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level,” Knowledge-Based Systems, vol. 108, pp. 110–124, 2016.
[2] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, 2009.
[3] J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011.
[4] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
[5] A. Esuli and F. Sebastiani, “SentiWordNet: A publicly available lexical resource for opinion mining,” in Proc. LREC, 2006, pp. 417–422.
[6] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[7] M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2004, pp. 168–177.
[8] C. Hutto and E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proc. Int. AAAI Conf. Web and Social Media, vol. 8, no. 1, pp. 216–225, 2014.
[9] F. H. Khan, U. Qamar, and S. Bashir, “A hybrid text mining approach for sentiment analysis,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 7, pp. 208–215, 2016.
[10] Y. Kim, “Convolutional neural networks for sentence classification,” in Proc. EMNLP, 2014, pp. 1746–1751.
[11] S. Kokolakis, “Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon,” Computers & Security, vol. 64, pp. 122–134, 2017.
[12] B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
[13] D. Maynard and M. A. Greenwood, “Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis,” in Proc. LREC, 2014, pp. 4238–4243.
[14] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014.
[15] F. Å. Nielsen, “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs,” in Proc. ESWC Workshop on Making Sense of Microposts, 2011, pp. 93–98.
[16] K. Nigam, J. Lafferty, and A. McCallum, “Using maximum entropy for text classification,” in Proc. IJCAI-99 Workshop on Machine Learning for Information Filtering, 1999, pp. 61–67.
[17] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” in Proc. LREC, 2010, pp. 1320–1326.
[18] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–135, 2008.
[19] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proc. EMNLP, 2002, pp. 79–86.
[20] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Computational Linguistics, vol. 37, no. 2, pp. 267–307, 2011.
[21] D. Tang, B. Qin, and T. Liu, “Learning sentiment-specific word embedding for Twitter sentiment classification,” in Proc. ACL, 2015, pp. 1555–1565.
[22] P. D. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews,” in Proc. ACL, 2002, pp. 417–424.
[23] M. Wiegand, A. Balahur, B. Roth, D. Klakow, and A. Montoyo, “A survey on the role of negation in sentiment analysis,” in Proc. Workshop on Negation and Speculation in Natural Language Processing, 2010, pp. 60–68.
[24] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” in Proc. HLT/EMNLP, 2005, pp. 347–354.
How to Cite This Paper
Duru Juliet Chinenye, Ogbuagu Chinedu Samuel, Chima Aguocha Obingonye, Praise Madumere Chukwubueze (2026). A LIGHTWEIGHT CLIENT-SIDE SENTIMENT ANALYSIS TOOL FOR REAL-TIME PRODUCT REVIEW CLASSIFICATION. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
