Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

A Sentiment Analysis Method Based on Emoticons and Sentiment Words

Authors
Baolin Gao, Zhiguo Zhou, Mingxue Zou, Chunyan Deng
Corresponding Author
Baolin Gao
Available Online April 2016.
DOI
https://doi.org/10.2991/emim-16.2016.281How to use a DOI?
Keywords
Emoticons; Sentiment words; Sentiment classification; Weibo
Abstract
Weibo, a Twitter-like online social network in China. The statistical analysis indicates that, most weibo users will use the emoticons when they release tweets. And it is more intuitive to observe the user's sentiment attitude through these emoticons. The purpose of this paper is dividing the weibo text into four categories: happy, sad, angry and disgust, according to the expression of emotion and attitude. This paper uses the Naive Bayes classification method, in the division on the training set, collect 165 commonly used emoticons and 200 sentiment words.According to the emotion intensity that the emoticons and emotion words expressed, give various weights. By computing the emoticons and sentiment words’ weighted average value, determine the classification. At the same time, use the category balance method to keep the proportion of four categories balance in the training set, to prevent classifier deviation. Through the experiments, the classification accuracy can reach 78.89%. Indicate that this method can get a better result in weibo text sentiment classification.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2016
ISBN
978-94-6252-176-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/emim-16.2016.281How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Baolin Gao
AU  - Zhiguo Zhou
AU  - Mingxue Zou
AU  - Chunyan Deng
PY  - 2016/04
DA  - 2016/04
TI  - A Sentiment Analysis Method Based on Emoticons and Sentiment Words
PB  - Atlantis Press
SP  - 1380
EP  - 1383
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.281
DO  - https://doi.org/10.2991/emim-16.2016.281
ID  - Gao2016/04
ER  -