Vibe Tracking: Customer Review Insight Platform

Abstract

Vibe Tracking is a web?based platform that automates the end?to?end process of extracting actionable insights from customer reviews on e?commerce and marketing sites. By combining a Flask?powered interface with a fine?tuned BERT sentiment classifier, the system ingests raw text—whether via CSV uploads or real?time streams—cleans and tokenizes it, and assigns each comment to one of five sentiment categories, from “Very Negative” to “Very Positive.” Beyond overall sentiment scoring, Vibe Tracking applies aspect?based decomposition and to pic modelling to isolate feedback on specific product features (e.g., battery life, design, price), enabling granular analysis of customer concerns and praise. Attention?heatmaps and confidence scores provide interpretability, helping stakeholders understand and trust each prediction. Designed for scalability and ease of use, Vibe Tracking empowers marketing and product teams to monitor brand perception continuously, prioritize feature improvements, and make data?driven decisions that enhance customer satisfaction and competitive advantage. Index Terms: Sentiment Analysis, Customer Reviews, BERT, Flask, NLP, Product Feedback, Aspect-based Sentiment Analysis, Topic Modelling, Machine Learning, E-commerce, Marketing Analytics, Real-time Data Processing, Multilingual Support, Predictive Analytics, Data Visualization, Customer Satisfaction, Competitive Intelligence, Web Application, Natural Language Processing (NLP), Edge Computing, Interpretability, Data-driven Decision Making.

Introduction

1. Overview

Vibe Tracking is an advanced, web-based platform designed to help businesses derive actionable insights from customer reviews. It automates the sentiment analysis process using state-of-the-art AI models, enabling organizations to understand customer feedback at scale.

2. Key Features

• Multi-source Data Ingestion: Supports CSV uploads and integrates real-time review data from platforms like Amazon, Yelp, Google Reviews, and social media.

• Text Preprocessing: Automatically cleans and tokenizes review text, filters noise (e.g., slang, emojis), and detects language for multilingual processing.

• Sentiment Classification: Uses fine-tuned BERT-based models (and DistilBERT for speed) to classify reviews on a 5-point sentiment scale: Very Negative to Very Positive.

• Aspect-Based Analysis: Extracts opinions tied to specific product features (e.g., price, battery life).

• Trend Detection: Applies keyword clustering and topic modeling to surface emerging trends.

• Interactive Dashboard: Offers real-time visualizations (sentiment graphs, heatmaps, filters) and custom alerts for sudden sentiment shifts.

3. Mathematical Model

• Objective: Given a review x(i)x^{(i)}x(i), predict sentiment label y(i)∈{1,2,3,4,5}y^{(i)} \in \{1, 2, 3, 4, 5\}y(i)∈{1,2,3,4,5} using a learned function f(x;θ)f(x; \theta)f(x;θ).

• Input Representation: Reviews are tokenized and embedded, then processed through a BERT encoder.

• Classification: The [CLS] token embedding is used for prediction via a softmax function.

4. Literature Support

The platform builds on:

• BERT and its variants for context-aware sentiment analysis.

• Studies showing cross-lingual sentiment effectiveness using multilingual BERT.

• Research optimizing BERT for real-time applications and user feedback analysis.

• Work exploring explainability in sentiment models for bias detection and transparency.

5. Objectives & Methodology

• Goal: Build a Flask-based web tool for uploading and analyzing comments.

• Steps:

  • Upload and validate CSV data

  • Preprocess comments

  • Run sentiment analysis with multilingual BERT

  • Display classified results in the interface

6. Results

Model performance was evaluated using accuracy and loss curves over training epochs. A successful model showed rising accuracy and falling loss, with a narrow gap between training and test performance, indicating strong generalization.

Conclusion

Vibe Tracking successfully demonstrates how modern transformer?based models can be leveraged to automate and scale the analysis of customer reviews, transforming unstructured feedback into actionable business intelligence. By integrating a fine?tuned BERT sentiment classifier with a user?friendly Flask interface, the platform delivers both overall sentiment scores and aspect?level insights, enabling marketing and product teams to rapidly identify strengths, weaknesses, and emerging trends.

References

[1] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. [2] NlpTown (2020). Multilingual BERT for Sentiment Analysis. HuggingFace Model Repository. https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment [3] Sun, C., Qiu, X., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? arXiv preprint arXiv:1905.05583. [4] Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762 [5] Wolf, T., et al. (2020). Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on EMNLP. https://aclanthology.org/2020.emnlp-demos.6 [6] Flask Documentation (2023). Flask Web Framework. https://flask.palletsprojects.com/ [7] Liu, Y., et al. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692 [8] Lan, Z., et al. (2020). ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR 2020. https://arxiv.org/abs/1909.11942 [9] Sanh, V., et al. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 [10] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf

Copyright

Copyright © 2025 Abhishek Kulkarni , Mayur Khedkar, Pranjali Gawade, Tanishka Kirloskar, Rohini Jadhav. 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.