TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A novel classification approach based on Naïve Bayes for Twitter sentiment analysis |
| 영문제목(English Title) |
A novel classification approach based on Naïve Bayes for Twitter sentiment analysis |
| 저자(Author) |
Junseok Song
Kyung Tae Kim
Byungjun Lee
Sangyoung Kim
Hee Yong Youn
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| 원문수록처(Citation) |
VOL 11 NO. 06 PP. 2996 ~ 3011 (2017. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With rapid growth of web technology and dissemination of smart devices, social networking service(SNS) is widely used. As a result, huge amount of data are generated from SNS such as Twitter, and sentiment analysis of SNS data is very important for various applications and services. In the existing sentiment analysis based on the Naïve Bayes algorithm, a same number of attributes is usually employed to estimate the weight of each class. Moreover, uncountable and meaningless attributes are included. This results in decreased accuracy of sentiment analysis. In this paper two methods are proposed to resolve these issues, which reflect the difference of the number of positive words and negative words in calculating the weights, and eliminate insignificant words in the feature selection step using Multinomial Naïve Bayes(MNB) algorithm. Performance comparison demonstrates that the proposed scheme significantly increases the accuracy compared to the existing Multivariate Bernoulli Naïve Bayes(BNB) algorithm and MNB scheme.
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| 키워드(Keyword) |
Twitter sentiment analysis
Machine learning
Naive Bayes
Attribute weighting
Feature selection
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| 파일첨부 |
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