Sentiment classification via supplementary information modeling

Zenan XU, Yetao FU, Xingming CHEN, Yanghui RAO*, Haoran XIE, Fu Lee WANG, Yang PENG

*Corresponding author for this work

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

2 Citations (Scopus)


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 languageEnglish
Title of host publicationWeb and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I
EditorsYi CAI, Yoshiharu ISHIKAWA, Jianliang XU
PublisherSpringer International Publishing AG
Number of pages9
ISBN (Electronic)9783319968902
ISBN (Print)9783319968896
Publication statusPublished - 2018
Externally publishedYes
EventAsia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data - Macau, Macao
Duration: 23 Jul 201825 Jul 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAsia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data
Abbreviated titleAPWeb-WAIM 2018
Internet address

Bibliographical note

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.


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