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 nongrammatical 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 nd 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 classi cation 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 over tted. Experimental results show that DualSentiNet signi cantly outperforms competitors in terms of both document sentiment classi cation accuracy and the word sentiment regression RMSE. In addition, DualSentiNet produces better word embedding by re"ecting both word and document sentiments.
|Title of host publication||Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication, IMCOM 2018|
|Publisher||Association for Computing Machinery|
|Publication status||Published - 2018 Jan 5|
|Event||12th International Conference on Ubiquitous Information Management and Communication, IMCOM 2018 - Langkawi, Malaysia|
Duration: 2018 Jan 5 → 2018 Jan 7
|Name||ACM International Conference Proceeding Series|
|Conference||12th International Conference on Ubiquitous Information Management and Communication, IMCOM 2018|
|Period||18/1/5 → 18/1/7|
Bibliographical noteFunding Information:
This research was supported by 1) the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1E1A1A01942642), 2) the Industrial Core Technology Development Program (10049079, Development of Mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy(MOTIE, Korea), and 3) the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2017-R0346-16-1007) supervised by the IITP(Institute for Information & communications Technology Promotion)
© 2018 ACM.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications