Improving analysis and prediction of customer reviews using NLP and Bernoulli Classifier
Priya Shahane
, G.P. Chakote
Opinion analysis, Sentiment analysis, Machine Learning Algorithm, Stanford Classifier, Reviews, E-commerce
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.
"Improving analysis and prediction of customer reviews using NLP and Bernoulli Classifier", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 6, page no.344 - 348, June-2018, Available :https://ijsdr.org/papers/IJSDR1806055.pdf
Volume 3
Issue 6,
June-2018
Pages : 344 - 348
Paper Reg. ID: IJSDR_180431
Published Paper Id: IJSDR1806055
Downloads: 000347188
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
