DocumentCode :
3739306
Title :
An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis
Author :
Yun Wan;Qigang Gao
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
fYear :
2015
Firstpage :
1318
Lastpage :
1325
Abstract :
In airline service industry, it is difficult to collect data about customers´ feedback by questionnaires, but Twitter provides a sound data source for them to do customer sentiment analysis. However, little research has been done in the domain of Twitter sentiment classification about airline services. In this paper, an ensemble sentiment classification strategy was applied based on Majority Vote principle of multiple classification methods, including Naive Bayes, SVM, Bayesian Network, C4.5 Decision Tree and Random Forest algorithms. In our experiments, six individual classification approaches, and the proposed ensemble approach were all trained and tested using the same dataset of 12864 tweets, in which 10 fold evaluation is used to validate the classifiers. The results show that the proposed ensemble approach outperforms these individual classifiers in this airline service Twitter dataset. Based on our observations, the ensemble approach could improve the overall accuracy in twitter sentiment classification for other services as well.
Keywords :
"Twitter","Bayes methods","Support vector machines","Classification algorithms","Sentiment analysis","Training","Data mining"
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2015 IEEE International Conference on
Electronic_ISBN :
2375-9259
Type :
conf
DOI :
10.1109/ICDMW.2015.7
Filename :
7395820
Link To Document :
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