REVIEW PREDICTION USING SENTIMENTAL ANALYSIS FOR MULTIPLE E-SHOPPING WEBSITES
DOI:
https://doi.org/10.61841/rceyxg74Keywords:
review prediction using sentimental analysis For multiple e-shopping websitesAbstract
Digital reviews play a crucial role in enhancing global communications Therefore, the sellers of digital reviews are sometimes benefitted , but always the loss is for the customers (online purchasers) due to the fake data. E-commerce acts like Amazon, Flipkart, etc. provide a platform to consumers to share their experience and provide real insights about the achievement of the product to future buyers. In order to extract valuable insights from an outsized set of reviews, classification of reviews into negative and positive sentiment is needed. Sentiment Analysis may be a computational study to excerpt subjective information from the text. In this work, Sentiment analysis or opinion mining is one of the primary tasks of NLP (Natural Language Processing). In recent years , to review any product or about web site Sentiment analysis play a vital role. In this paper, we focus on implements the matter of sentiment polarity categorization, which is surrounded by the elemental problems of sentiment analysis. polarity categorization is proposed in a general process for sentiment with detailed process descriptions. The usage of data in this study are online product reviews collected from various e-shopping websites like Amazon.com, Flipkart, ePay, etc. Product reviews were pre-processed using text processing techniques. In pre-processing, the Product review files are generated as a flat-file. The flat file is tokenized into sentences and the keywords are listed after removing the stop words. We have identified the frequency of each word and extracted the topic which has the highest frequency count. Similar comments in each topic are clustered and then the clustered words are classified into positive or negative comments. The classified comments are generated as a chart for straightforward visualization.
Downloads
References
1. AnshulMittal, ArpitGoel in-stock prediction using Twitter sentiment analysis.
2. Brennan, J. & Williams, R. (2004) Collecting and Using Student Product review. A Guide to Good Practice (LTSN, York).
3. Dhanalakshmi V in opinion mining from student product review data using supervised learning algorithms.
4. Elaine Keane & Iain Mac Labhrainn, Obtaining Student Product Review on Teaching & Course Quality, CELT, April 2005.
5. GokarnIlaNitin ,Asst.Prof.GottipatiSwapna , Prof.VenkyShankararaman in Analyzing Educational notes on Sentiments and Topics: A Text Analytics Approach.
6. GokarnIlaNitin ,Asst.Prof.GottipatiSwapna , Prof.VenkyShankararaman in Analyzing Educational opinions for Topics and Sentiments: A Text Analytics Approach.
7. Harshali P. Patil, Mohammed Atique in Sentiment Analysis for Social Media.
8. Tan Li Im, PhangWai San, and Chin Kim in greatness in Semantic Agents Universities Malaysia in Rule-based Sentiment Analysis for Financial News
9. Mark McGuire, Constance Kampf, and Aarhus University in Proving Social Media Sentiment Analysis to Understand Audiences: A New Skill for Technical Communicators.
10. M.S. Neethu, R. Rajasree in sentiment analysis on Twitter using machine learning techniques.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
