NDLI: Sentiment Analysis on Unstructured Review

Content Provider	IEEE Xplore Digital Library
Author	Nithya, R. Maheswari, D.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Studies(PG), RVS Coll. of Arts & Sci., Sulur, India (Maheswari, D.) \|\| Sch. of Comput. Studies(UG), RVS Coll. of Arts & Sci., Sulur, India (Nithya, R.)
Abstract	Sentiment analysis mainly focuses on subjectivity and polarity detection. Today consumers make buying decision based on the customer's review that is available in some of the online shopping sites like shopclues, fabfurnish, pepperfry, flipkart etc. There are also some of the specific websites which discuss about positive and negative facts of those products that comes to market like reevoo, buzzillions, bizarte, amazon etc. Hence this type of analysis are socially very needed for sellers to undergo market analysis, branding, product penetration, market segmentation and so on. Here, the proposed paper classifies the most identified features using supervised learning method Naive Bayes and determined their positive, negative and neutral polarity distribution.
Starting Page	367
Ending Page	371
File Size	625519
Page Count	5
File Format	PDF
ISBN	9781479939664
DOI	10.1109/ICICA.2014.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sentiment Classification Sentiment analysis Supervised learning Media Feature extraction Educational institutions Batteries Opinion mining Data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Recommended or Not Recommended? Review Classification through Opinion Extraction

Opinion mining and analysis: A literature review

Feature based opinion mining: A survey

A framework for informal language: Opinion mining

Classifying Product Reviews from Balanced Datasets for Sentiment Analysis and Opinion Mining

Exploring weakly supervised latent sentiment explanations for aspect-level review analysis

Influence factor based opinion mining of Twitter data using supervised learning

Toward a fuzzy domain sentiment ontology tree for sentiment analysis

An Experimental Study of Feature Extraction Techniques in Opinion Mining

Sentiment Analysis on Unstructured Review

Similar Documents

Recommended or Not Recommended? Review Classification through Opinion Extraction

Opinion mining and analysis: A literature review

Feature based opinion mining: A survey

A framework for informal language: Opinion mining

Classifying Product Reviews from Balanced Datasets for Sentiment Analysis and Opinion Mining

Exploring weakly supervised latent sentiment explanations for aspect-level review analysis

Influence factor based opinion mining of Twitter data using supervised learning

Toward a fuzzy domain sentiment ontology tree for sentiment analysis

An Experimental Study of Feature Extraction Techniques in Opinion Mining

Sentiment Analysis on Unstructured Review