Paper Info
Title
DualSentiNet: Dual Prediction of Word and Document Sentiments Using Shared Word Embedding
Abstract
With the popularization of social networking services, numerous words are newly emerging every day in personalized document sources. Slang terms, abbreviations, newly coined words, and non-grammatical words or expressions belong here, and people are more likely to use these words with a certain sentimental tendency compared to other standard words. Thus, it becomes important to find their meanings or sentiments to analyze the sentiment of user-generated texts. This paper proposes a novel sentiment analysis model, termed DualSentiNet, which predicts the sentiments of newly emerged words and documents at the same time. Our model is composed of three parts: (i) a word-level sentiment regression network, (ii) a document-level sentiment classification network, and (iii) a shared word embedding layer. DualSentiNet makes a word embedding layer shared by two different networks, thereby learning richer information about both word-level and document-level sentiments through two-way back-propagation. Consequently, it improves the performance of sentiment prediction by preventing word vectors from being overfitted. Experimental results show that DualSentiNet significantly outperforms competitors in terms of both document sentiment classification accuracy and the word sentiment regression RMSE. In addition, DualSentiNet produces better word embedding by reflecting both word and document sentiments.
Year
DOI
Venue
2018
10.1145/3164541.3164629
IMCOM
Keywords
Field
DocType
Sentiment analysis, Word embedding, Deep learning
Social network,Expression (mathematics),Computer science,Sentiment analysis,Computer network,Natural language processing,Artificial intelligence,Word embedding,Deep learning,Slang
Conference
ISBN
Citations 
PageRank 
978-1-4503-6385-3
0
0.34
References 
Authors
20
5
Name
Order
Citations
PageRank
Dongha Lee1146.77
Hyunjun Ju212.05
Hwanjo Yu31715114.02
Jung-Mi Park400.68
Kye-Yoon Kim510.69