NDLI: A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

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A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

Content Provider	ACM Digital Library
Author	Dey, Shubhamoy Sharma, Anuj
Abstract	The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain, and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.
Starting Page	67
Ending Page	75
Page Count	9
File Format	PDF
ISSN	15596915 19310161
DOI	10.1145/2432546.2432552
Journal	ACM SIGAPP Applied Computing Review (SIAP)
Volume Number	12
Issue Number	4
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sentiment analysis Bpann Classification Sentiment lexicon
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

An artificial neural network based approach for sentiment analysis of opinionated text

1 Overview Goals Plan Sentiment lexicons

Sentiment lexicon construction using SentiWordNet 3.0

Chapter 20 generating, refining and using sentiment lexicons.

A Hybrid Approach to Sentiment Analysis Enhanced by Sentiment Lexicons and Polarity Shifting Devices

Building Large-Scale Twitter-Specific Sentiment Lexicon: A Representation Learning Approach

Sentiment word identification using the maximum entropy model

Adapting Sentiment Lexicons using Contextual Semantics for Sentiment Analysis of Twitter

Data from: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme (Dataset)

A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

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