TY - GEN
T1 - Sentiment classification via supplementary information modeling
AU - Xu, Zenan
AU - Fu, Yetao
AU - Chen, Xingming
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Wang, Fu Lee
AU - Peng, Yang
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Intensity words
KW - Negation words
KW - Sentiment supplementary information
UR - http://www.scopus.com/inward/record.url?scp=85050548597&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-96890-2_5
DO - 10.1007/978-3-319-96890-2_5
M3 - Conference contribution
AN - SCOPUS:85050548597
SN - 9783319968896
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 62
BT - Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
A2 - Xu, Jianliang
A2 - Ishikawa, Yoshiharu
A2 - Cai, Yi
T2 - 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Y2 - 23 July 2018 through 25 July 2018
ER -