ReviewXplorer: Intelligent Sentiment Analytics for Web Content

Abstract

SentimentAnalysis of Online User Reviews. The product review online has been a major source of customer information in deciding whether to purchase a product or not. Ecommerce sites that are popular may have thousands of reviews on a single product and this makes it tedious to individuals to go through it swiftly and summarize them to get an overall view. Sentiment analy- sis offers a mechanical means to generalize the general opinions. This paper introduces ReviewXplorer, which isa method to examine Amazon product reviews so as to determine theexpressed sentimentand emotionaltone by the users. Reviews are scraped directly based on product URLs, undergone some text processing operations and subsequently the classification using a transformer based language model. Besides rating the ratings as positive, neutral,andnegative,italsodetectstheexistenceofemo- tionslikeanger,sadness,andjoy.Thenumberoferroneous predictionsmaybereducedbysarcasmdetectionwhichis prevalentinmostexistingmodelswithsarcasm.Theoverall sentiment trend is given in the form of the final visual, simple results. The given approach is a realistic solutionof sentiment analysis on the bulk of online reviews and is advantageous to both the customers and organizations.

Introduction

The text describes ReviewXplorer, an AI-based system designed to analyze large volumes of online product reviews (especially from platforms like Amazon) and extract meaningful insights from them.

It addresses the problem that users face information overload, where thousands of reviews are difficult to read and manually analyze. Traditional sentiment analysis methods (like Naive Bayes and Logistic Regression) struggle with informal language, sarcasm, and complex emotions, making them less effective for real-world reviews.

ReviewXplorer solves this by using a modern transformer-based NLP approach (BERT) along with a complete pipeline that includes:

• Web scraping to automatically collect reviews from product URLs
• Text preprocessing to clean and normalize noisy user-generated text
• Sentiment analysis to classify reviews as positive, negative, or neutral
• Emotion detection to identify feelings like happiness, anger, sadness, or surprise
• Sarcasm detection to correctly interpret indirect or misleading opinions
• Visualization tools to present results through charts and dashboards

The system also provides aggregation of results, helping users and businesses quickly understand customer opinions, trends, and satisfaction levels without reading individual reviews.

Conclusion

This paper developed a sentiment analysis system knownasRe-viewXplorerthatwillhavethecapabilitytoscan many online product reviews and extract useful informationin them automatically. The system will scrape the web to extract reviews directly on the Amazon product URLs. It then proceeds with a process of text processing the data, including text preprocessing, sentiment analysis, emotion analysis, and sarcasm analysis. By joining the pieces together in a single pipeline, the proposedsystemwillbecapableofdeterminingnotonly thepolarityofreviewsbuttheemotionaltonerepresented by users posting the reviews. Transformer-based language mod- els are used to improve the ability of the system to understand the context of the sentences. Unlike conventional machine learning methods, this helps the model to make a more accurate interpretation of the opinion of the user. Also, the implemented use of sarcasm detection lowers misclassification caused due to the sarcastic remarks whichare common in online reviews. The results of the experiment indicate that the proposed system may effectively summarize the opinions of the population because it allows considering large groups of product reviews and presenting the resultswith the help of visual effects. The system is of help to businesses and consumers alike due to the better way it provides of understanding customer satisfaction trends and product perception. Everything said and done, the proposed ReviewXplorer frame- work presents an effective and reliable way of analyzing the types of content created by users and drawing relevant conclusions based on product reviews on the Internet.

References

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Copyright

Copyright © 2026 Tangudu Harshith , Charitha Ramireddy, M. Kalyan Babu , Ms. Neelam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.