NDLI: A hybrid approach to identifying sentiment polarity for new words

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2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE)

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Table of contents

Wireless VITAE 2013

2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE)

2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology

A hybrid approach to identifying sentiment polarity for new words

Content Provider	IEEE Xplore Digital Library
Author	Yang Yang Ruifan Li Yanquan Zhou
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., Beijing Univ. of Posts & Telecommun., Beijing, China (Yang Yang; Ruifan Li; Yanquan Zhou)
Abstract	Microblog is a typical form of heterogeneous information. For this information, identifying sentiment polarity of new words plays a fundamental role in sentiment analysis. In this paper, we proposed a hybrid approach using both statistic and syntax information to identifying the sentiment polarity of new words. We first filter the raw tweets out some noises and segment the clean data with POS tagging. Next, we collect new words by filtering rules. Then, we assign each new word with a polarity using both statistics and patterns information. We evaluate our approach on a real dataset from Sina Weibo, achieving a relatively high F-score of 0.241 compared with the baseline of 0.22.
Starting Page	1
Ending Page	5
File Size	741127
Page Count	5
File Format	PDF
ISBN	9781479946266
e-ISBN	9781479946242
DOI	10.1109/VITAE.2014.6934435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-11
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Sentiment polarity New words Noise measurement Patterns
Content Type	Text
Resource Type	Article

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