NDLI: Sentiment Analysis and Summarization of Twitter Data

Content Provider	IEEE Xplore Digital Library
Author	Bahrainian, S.-A. Dengel, A.
Copyright Year	2013
Description	Author affiliation: Comput. Sci. Dept., Univ. Of Kaiserslautern, Kaiserslautern, Germany (Bahrainian, S.-A.; Dengel, A.)
Abstract	Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is product-based sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented (i.e. aspect-based) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features.
Starting Page	227
Ending Page	234
File Size	281100
Page Count	8
File Format	PDF
ISBN	9780769550961
DOI	10.1109/CSE.2013.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Dictionaries Opinion Mining Feature extraction Twitter Generators Classification algorithms Sentiment Analysis Sentiment Summarization
Content Type	Text
Resource Type	Article

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