UPM Institutional Repository

Prediction of rating from comments based on information retrieval and sentiment analysis


Citation

Alshari, Eissa Mohammed Mohsen and Azman, Azreen and Mustapha, Norwati and C. Doraisamy, Shyamala and Alksher, Mostafa Ahmed (2016) Prediction of rating from comments based on information retrieval and sentiment analysis. In: 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP'16), 23-24 Aug. 2016, Hatten Hotel Melaka, Malaysia. (pp. 32-36).

Abstract

The number of users of an on-line shopping websites is continuously increasing. Such website often provides facility for the users to give comments and ratings to the products being sold on the websites. This information can be useful as the recommendation for other users in making their purchase decision. This paper investigates the problem of predicting rating based on users' comments. A classifier based on information retrieval model is proposed for the prediction. In addition, the effect of integrating sentiment analysis for the rating prediction is also investigated. Based on the results, an improvement in prediction performance can be expected with sentiment analysis where an increase of 54% is achieved.


Download File

[img]
Preview
PDF (Abstract)
Prediction of rating from comments based on information retrieval and sentiment analysis.pdf

Download (33kB) | Preview

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/INFRKM.2016.7806330
Publisher: IEEE
Keywords: Comments; Information retrieval; Rating; Sentiment analysis; Vector space model
Depositing User: Nabilah Mustapa
Date Deposited: 30 Jun 2017 09:52
Last Modified: 30 Jun 2017 09:52
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/INFRKM.2016.7806330
URI: http://psasir.upm.edu.my/id/eprint/55910
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item