NDLI: Arabic sentiment polarity identification using a hybrid approach

Content Provider	IEEE Xplore Digital Library
Author	Khasawneh, R.T. Wahsheh, H.A. Alsmadi, I.M. AI-Kabi, M.N.
Copyright Year	2015
Description	Author affiliation: CIS Dept., Jordan Univ. of Sci. & Technol., Irbid, Jordan (Khasawneh, R.T.) \|\| Comput. Sci. Dept., King Khaled Univ., Abha, Saudi Arabia (Wahsheh, H.A.) \|\| Comput. Sci. Dept., Boise State Univ., Boise, ID, USA (Alsmadi, I.M.) \|\| Fac. of Sci. & IT, Zarqa Univ., Zarqa, Jordan (AI-Kabi, M.N.)
Abstract	Recent years witness a significant increase in research related to knowledge extraction from web social networks or media. The enormous volume of posted comments, and related media can be a rich source of information. In Middle East and the Arab world in particular, social media websites continue to be the top visited websites especially with the current social and political changes in this part of the world. Sentiment analysis and opinion mining focus on identifying and evaluating positive and negative opinions and comments. This study aims to identify the sentiment polarity for collected comments or posts from Twitter using a hybrid approach and a modest dataset of Arabic (Text and audio) comments. Two machine learning classification techniques are used to perform the required classification to identify the polarity of the collected opinions. We extended the evaluation of prediction algorithms and enhance them using Bagging and Boosting algorithms. We extracted a unified dataset of texts, audios and images and applied processing methods to extract final sentiment opinions. We noticed that some special expressions specially in recoding (such as laughing, yelling, etc. within the recording) have a negative effect on the accuracy of the automatic sentiment prediction system.
Starting Page	148
Ending Page	153
File Size	675741
Page Count	6
File Format	PDF
ISBN	9781479973491
DOI	10.1109/IACS.2015.7103218
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-07
Publisher Place	Jordan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economics Sentiment analysis Dictionaries Audio sentiments Media Document level Boosting Twitter Social networks Opinion mining Bagging Arabic Textual classification
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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