Predicto - Review Rate Predictor

Abstract

This paper presents a new Chrome extension for predicting star ratings according to the customer's review. Predicto mainly deals with analyzing customer feedback to predict star ratings can provide valuable insights to both consumers and businesses. This research paper presents the development of a Chrome extension designed to predict star ratings based on customer reviews. Leveraging logistic regression as the predictive model, the extension employs natural language processing (NLP) techniques to extract pertinent features from textual feedback. The proposed Chrome extension capitalizes on web scraping capabilities to gather and preprocess customer reviews from diverse online sources. This research contributes to the field of sentiment analysis, customer feedback evaluation, and web scraping by presenting a practical implementation in the form of a user-friendly Chrome extension. The extension's utilization of logistic regression enhances its prediction capabilities and offers a valuable tool for enhancing the online shopping experience and review analysis.

Country : India

1 Sourav Chanda2 Abhishek Pandey3 Priyanka Mondal

  1. Department of Computer Science & Engineering, University of Calcutta, Kolkata, India
  2. Software Engineer Intern, Socielo Tech, Kolkata, India
  3. Department of Information Technology (Internet of Things), Maulana Abul Kalam Azad University of Technology, Kolkata, India

IRJIET, Volume 7, Issue 12, December 2023 pp. 132-136

doi.org/10.47001/IRJIET/2023.712018

References

[1]

Y. W, C.  W, Jiangang Nan, "Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation," Chinese Journal of Electronics, vol. 32, no. 4, pp. 932 - 940, 2023.

[2]

N. K, D. Z, K. S, Nan Yang, "Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach," Annals of Operations Research, 2023.

[3]

A. C, D. G, A. K, Maaz Bin Shahid, "Review Based Rating Prediction using Machine Learning Techniques," nternational Conference on System Modeling & Advancement in Research Trends (SMART), pp. 118-122, 2022.

[4]

A. S. G, Basem. H. A. Ahmed, "Review Rating Prediction Framework Using Deep Learning," Journal of Ambient Intelligence and Humanized Computing, 2022.

[5]

M. B. B, Karlo Puh, "Predicting sentiment and rating of tourist reviews using machine learning," Emerald Insight, vol. 6, no. 3, pp. 1188-1204, 2022.

[6]

T. B, Ankit Taparia, "Sentiment Analysis: Predicting Product Reviews’ Ratings using Online Customer Reviews," Social Science Research Network, 2020.

[7]

S. X, Y. H, X. L, Bingkun Wang, "Review Rating Prediction Based on User Context and Product Context," Multidisciplinary Digital Publishing Institute, vol. 8, no. 10, pp. 01-13, 2018.

[8]

L. I. S, Sasikala P, "Sentiment Analysis and Prediction of Online Reviews with Empty Ratings," Research India Publications, 2018.

[9]

V. S. K, G. A, N. V, M. Pravallika Reddy, "Review-based Rating Prediction," INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS, vol. 6, no. 1, pp. 843-846, 2018.

[10]

S. U, T. N, S. G, A. NithyaKalyani, "Rating prediction using textual reviews," Journal of Physics: Conference Series, 2018.