A topic-enhanced recurrent autoencoder model for sentiment analysis of short texts
by Shaochun Wu; Ming Gao; Qifeng Xiao; Guobing Zou
International Journal of Internet Manufacturing and Services (IJIMS), Vol. 7, No. 4, 2020

Abstract: This paper presents a topic-enhanced recurrent autoencoder model to improve the accuracy of sentiment classification of short texts. First, the concept of recurrent autoencoder is proposed to tackle the problems in recursive autoencoder including 'increasing in computation complexity' and 'ignoring the natural word order'. Then, the recurrent autoencoder model is enhanced with the topic and sentiment information generated by joint sentiment-topic (JST) model. Besides, in order to identify the negations and ironies in short texts, sentiment lexicon is utilised to add feature dimensions for sentence representations. Experiments are performed to determine the feasibility and effectiveness of the model. Compared with recursive autoencoder model, the classification accuracy of our model is improved by about 7.7%.

Online publication date: Mon, 12-Oct-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Internet Manufacturing and Services (IJIMS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com