research-article

DualSentiNet: Dual Prediction of Word and Document Sentiments Using Shared Word Embedding

Authors:

Kye-Yoon KimAuthors Info & Claims

IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication

Article No.: 34, Pages 1 - 9

https://doi.org/10.1145/3164541.3164629

Published: 05 January 2018 Publication History

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.

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Cited By

Asmita De Mishra S(2022)Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care PerspectiveAugmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis10.1007/978-981-19-1076-0_12(205-235)Online publication date: 20-Apr-2022
https://doi.org/10.1007/978-981-19-1076-0_12

Index Terms

DualSentiNet: Dual Prediction of Word and Document Sentiments Using Shared Word Embedding
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
        Supervised learning by regression
    2. Machine learning approaches
      1. Neural networks

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IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication

January 2018

628 pages

ISBN:9781450363853

DOI:10.1145/3164541

Copyright © 2018 ACM.

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SKKU: SUNGKYUNKWAN UNIVERSITY

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Published: 05 January 2018

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IMCOM '18

IMCOM '18: The 12th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2018

Langkawi, Malaysia

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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Asmita De Mishra S(2022)Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care PerspectiveAugmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis10.1007/978-981-19-1076-0_12(205-235)Online publication date: 20-Apr-2022
https://doi.org/10.1007/978-981-19-1076-0_12

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