NDLI: Analysis of sentiments using unsupervised learning techniques

Content Provider	IEEE Xplore Digital Library
Author	Usha, M.S. Indra Devi, M.
Copyright Year	2013
Description	Author affiliation: KLN Coll. of Inf. Technol., Pottapalayam, Sivagangai, India (Usha, M.S.; Indra Devi, M.)
Abstract	Sentimental analysis, a sub discipline within data mining and computational linguistics, refers to the computational methodology for mining, understanding and assessing the opinions expressed in many opinion rich resources like blogs, discussion forums etc. The goal of sentiment analysis is to identify emotional states in online text. Most of the time classifiers trained in one domain do not perform well in another domain. Also the problem in existing approaches is not to detect sentiment and topics. Sentiments may vary with topics. This paper proposes a new model called Combined Sentiment Topic (CST) model to detect sentiments and topics simultaneously from text. This model is based on Gibbs sampling algorithm. Besides, unlike supervised approaches to opinion mining which often fail to produce good performance when shifting to other domains, the unsupervised nature of CST makes it highly portable to other domains. CST model performs better compared to existing semi- supervised approaches.
Sponsorship	IEEE Madras Sect.
Starting Page	241
Ending Page	245
File Size	209821
Page Count	5
File Format	PDF
ISBN	9781467357869
e-ISBN	9781467357883
DOI	10.1109/ICICES.2013.6508203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Sentiment analysis Accuracy Sentimental analysis Motion pictures Opinion mining Data mining Joints Joint Sentiment Topic Model (JST)
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

Weakly supervised sentiment analysis using joint sentiment topic detection with bigrams

HOMS: Hindi opinion mining system

Joint Sentiment/Topic extraction from text

Weakly Supervised Joint Sentiment-Topic Detection from Text

Detecting General Opinions from Customer Surveys

Predicting movie sales revenue using online reviews

Opinion mining and analysis: A literature review

Sentiment Analysis of Malayalam film review using machine learning techniques

A Joint Model for Topic-Sentiment Evolution over Time

Analysis of sentiments using unsupervised learning techniques

Similar Documents

Weakly supervised sentiment analysis using joint sentiment topic detection with bigrams

HOMS: Hindi opinion mining system

Joint Sentiment/Topic extraction from text

Weakly Supervised Joint Sentiment-Topic Detection from Text

Detecting General Opinions from Customer Surveys

Predicting movie sales revenue using online reviews

Opinion mining and analysis: A literature review

Sentiment Analysis of Malayalam film review using machine learning techniques

A Joint Model for Topic-Sentiment Evolution over Time

Analysis of sentiments using unsupervised learning techniques