Abstract
Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.
Original language | English |
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Title of host publication | Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I |
Editors | Yi CAI, Yoshiharu ISHIKAWA, Jianliang XU |
Publisher | Springer International Publishing AG |
Pages | 54-62 |
Number of pages | 9 |
ISBN (Electronic) | 9783319968902 |
ISBN (Print) | 9783319968896 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data - Macau, Macao Duration: 23 Jul 2018 → 25 Jul 2018 http://conferences.cis.umac.mo/apwebwaim2018/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10987 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data |
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Abbreviated title | APWeb-WAIM 2018 |
Country/Territory | Macao |
City | Macau |
Period | 23/07/18 → 25/07/18 |
Internet address |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG, part of Springer Nature 2018.
Funding
The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), Guangdong Science and Technology Program grant (2017A050506025), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong.
Keywords
- Intensity words
- Negation words
- Sentiment supplementary information