NDLI: Text mining facebook status updates for sentiment classification

Content Provider	IEEE Xplore Digital Library
Author	Akaichi, J. Dhouioui, Z. Lopez-Huertas Perez, M.J.
Copyright Year	2013
Description	Author affiliation: Comput. Sci. Dept., Inst. Super. de Gestion de Tunis (ISG), Le Bardo, Tunisia (Akaichi, J.; Dhouioui, Z.) \|\| Fac. de Biblioteconomia y Documentacion, Univ. de Granada, Granada, Spain (Lopez-Huertas Perez, M.J.)
Abstract	In recent years, text mining and sentiment analysis have received great attention due to the abundance of opinion data that exist in social networks such as Facebook, Twitter, etc. Sentiments are projected on these media using texts for expressing feelings such as friendship, social support, anger, happiness, etc. Existing sentiment analysis studies tend to identify user behaviors and state of minds but remain insufficient due to complexities in conveyed texts. In this research paper, we focus on the usage of text mining for sentiment classification. Illustration is performed on Tunisian users' statuses on Facebook posts during the “Arabic Spring” era. Our aim is to extract useful information, about users' sentiments and behaviors during this sensitive and significant period. For that purpose, we propose a method based on Support Vector Machine (SVM) and Naïve Bayes. We also construct a sentiment lexicon, based on the emoticons, interjections and acronyms', from extracted statuses updates. Moreover, we perform some comparative experiments between two machine learning algorithms SVM and Naïve Bayes through a training model for sentiment classification.
Starting Page	640
Ending Page	645
File Size	256215
Page Count	6
File Format	PDF
e-ISBN	9781479922284
DOI	10.1109/ICSTCC.2013.6689032
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-11
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Sentiment analysis Naïve Bayes Machine learning Feature extraction Social networks Classification algorithms Data mining Facebook
Content Type	Text
Resource Type	Article

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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
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