Welcome to IJSDR UGC CARE norms ugc approved journal norms IJRTI Research Journal | ISSN : 2455-2631
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Issue: January 2023

Volume 8 | Issue 1

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: Improving analysis and prediction of customer reviews using NLP and Bernoulli Classifier
Authors Name: Priya Shahane , G.P. Chakote
Unique Id: IJSDR1806055
Published In: Volume 3 Issue 6, June-2018
Abstract: Customer feedback is important in improving the company's services, both in terms of intimacy and openness. Open-minded reviews mean comments, expressions, and direct comments from customers. However, companies have a variety of content or groups to evaluate with their scores and overall scores for the types of services that many customers are looking for. The problem is that some customers give points to reviews. Other reviewers must read the comments and provide feedback that is different from the rating. So this article offers analysis and forecasts from open customer reviews using the probability classifier. Classifiers will use case studies of hotels with customer reviews in open reviews for training data to group feedback on whether mining is a positive or negative feedback. Data mining, commenting or opinion analysis is a part of data mining. Data mining is a form of natural language processing used to record people's attitudes toward a particular subject or product. Most mining reviews give the category a positive, neutral or negative review. Recently, data mining reviews have been very successful due to the availability of enormous amounts of rich web resource reviews. Digital formats such as forums, discussion sites, blog reviews, etc. When using ecommerce websites, rudely increased the users not only. Instead of buying products on the site, but also providing feedback and suggestions that will benefit other users, compiled user reviews will be analyzed and organized to make better decisions.
Keywords: Opinion analysis, Sentiment analysis, Machine Learning Algorithm, Stanford Classifier, Reviews, E-commerce
Cite Article: "Improving analysis and prediction of customer reviews using NLP and Bernoulli Classifier", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 6, page no.344 - 348, June-2018, Available :http://www.ijsdr.org/papers/IJSDR1806055.pdf
Downloads: 000201507
Publication Details: Published Paper ID: IJSDR1806055
Registration ID:180431
Published In: Volume 3 Issue 6, June-2018
DOI (Digital Object Identifier):
Page No: 344 - 348
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media
IJSDR

Indexing Partner