NDLI: A Statistical and Evolutionary Approach to Sentiment Analysis

Content Provider	IEEE Xplore Digital Library
Author	Carvalho, J. Prado, A. Plastino, A.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Univ. Fed. Fluminense, Niteroi, Brazil (Carvalho, J.; Prado, A.; Plastino, A.)
Abstract	In the past years, the Web has become a huge source of opinionative data. Social media, such as Twitter, are regarded as public diaries, where millions of people express their sentiments and opinions in their daily interaction. One of the biggest challenges in the analysis of such data, is the classification of their polarity, that is, whether they carry a positive or negative connotation. For this purpose, statistical methods have been inspired by the observation that if two words frequently appear together within the same context, they are likely to have the same polarity. Consequently, the polarity of a word can be determined by calculating its relative frequency of co-occurrence with special words, called paradigm words, whose polarities are invariantly preserved (e.g., "good" and "bad"). In this way, one can classify, for example, a tweet as carrying a positive polarity, if the majority of its words is more strongly associated with the word "good" than with the word "bad". In current statistical approaches, such paradigm words have been selected following different criteria, without any prior evaluation. Motivated by this observation, we propose to classify tweets via a statistical method where the paradigm words are selected by means of a genetic algorithm. This algorithm explores a set of paradigm words to find a subset of such words that leads to a significant improvement of the classification accuracy. Additionally, we believe that an appropriate set of paradigm words may vary according to the data domain. For example, paradigm words applied in the classification of tweets in the domain of movies may not be convenient to classify tweets related to products. We prove this assumption empirically, using tweets from different domains, and show that our approach deals properly with this problem.
Sponsorship	IEEE Comput. Soc.
Starting Page	110
Ending Page	117
File Size	258446
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-11
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	genetic algorithm opinion mining Accuracy Statistical analysis Semantics sentiment analysis Feature extraction Twitter Biological cells Equations Genetic algorithms
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

A Statistical and Evolutionary Approach to Sentiment Analysis

Sentiment Analysis and Summarization of Twitter Data

Feature based opinion mining: A survey

Metaheuristic algorithms for feature selection in sentiment analysis

Semantic orientation approach for sentiment classification

Twitter Mining in the Oil Business: A Sentiment Analysis Approach

Automatic image annotation using an evolutionary algorithm (IAGA)

Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach

SASM: A tool for sentiment analysis on Twitter

A Statistical and Evolutionary Approach to Sentiment Analysis

Similar Documents

A Statistical and Evolutionary Approach to Sentiment Analysis

Sentiment Analysis and Summarization of Twitter Data

Feature based opinion mining: A survey

Metaheuristic algorithms for feature selection in sentiment analysis

Semantic orientation approach for sentiment classification

Twitter Mining in the Oil Business: A Sentiment Analysis Approach

Automatic image annotation using an evolutionary algorithm (IAGA)

Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach

SASM: A tool for sentiment analysis on Twitter

A Statistical and Evolutionary Approach to Sentiment Analysis