Sentiment analysis of Chinese microblog based on stacked bidirectional LSTM

Yue LU, Junhao ZHOU, Hong Ning DAI, Hao WANG, Hong XIAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a sentiment analysis method by incorporating Continuous Bag-of-Words (CBOW) model and Stacked Bidirectional long short-term memory (Stacked Bi-LSTM) model to enhance the performance of sentiment prediction. Firstly, a word embedding model, CBOW model, is employed to capture semantic features of words and transfer words into high dimensional word vectors. Secondly, we introduce Stacked Bi-LSTM model to conduct the feature extraction of sequential word vectors at a deep level. Finally, a binary softmax classifier utilizes semantic and contextual features to predict the sentiment orientation. Extensive experiments on real dataset collected from Weibo (i.e., one of the most popular Chinese microblogs) show that our proposed approach achieves better performance than other machine learning models.

Original languageEnglish
Title of host publicationProceedings - 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages162-167
Number of pages6
ISBN (Electronic)9781538685341
DOIs
Publication statusPublished - 7 Feb 2019
Externally publishedYes
Event15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018 - Yichang, China
Duration: 16 Oct 201818 Oct 2018

Publication series

NameProceedings - 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018

Conference

Conference15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018
Country/TerritoryChina
CityYichang
Period16/10/1818/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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