TY - GEN
T1 - Not Only the Contextual Semantic Information
T2 - 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
AU - Hua, Liping
AU - Chen, Qinhui
AU - Huang, Zelin
AU - Zhao, Hui
AU - Zhao, Gang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Extremely short comments (ESC) often contain rich information to convey users’ emotions towards content. However, conducting sentiment analysis on ESC is challenging due to the limited contextual semantic information and colloquial expressions. Traditional methods mainly focus on contextual text features. In this work, we propose a novel model, named Chinese Phonetic-Attentive Deep Fusion Network (CPADFN) that attentively fuse the Chinese phonetic alphabet features of the ESC, meta-information about the ESC along with the contextual text features. First, the multi-head self-attention mechanism is utilized to obtain the phonetic alphabet representation and the sentence representation separately. Also, a fully-connected layer is used on the embeddings of the meta-information about the ESC to obtain the meta-information representation. Then, the local activation unit is employed to attentively fuse these feature representations. Bi-LSTM is applied to address the sequence dependency across these fused features separately. Third, a fully-connected layer with softmax function is applied to predict emotional labels. We conduct experiments on a self-crawled ESC dataset DanmuCorpus, and two public Chinese short text datasets, MovieReview and WeiboCorpus. The experimental results demonstrate that CPADFN achieves better performances.
AB - Extremely short comments (ESC) often contain rich information to convey users’ emotions towards content. However, conducting sentiment analysis on ESC is challenging due to the limited contextual semantic information and colloquial expressions. Traditional methods mainly focus on contextual text features. In this work, we propose a novel model, named Chinese Phonetic-Attentive Deep Fusion Network (CPADFN) that attentively fuse the Chinese phonetic alphabet features of the ESC, meta-information about the ESC along with the contextual text features. First, the multi-head self-attention mechanism is utilized to obtain the phonetic alphabet representation and the sentence representation separately. Also, a fully-connected layer is used on the embeddings of the meta-information about the ESC to obtain the meta-information representation. Then, the local activation unit is employed to attentively fuse these feature representations. Bi-LSTM is applied to address the sequence dependency across these fused features separately. Third, a fully-connected layer with softmax function is applied to predict emotional labels. We conduct experiments on a self-crawled ESC dataset DanmuCorpus, and two public Chinese short text datasets, MovieReview and WeiboCorpus. The experimental results demonstrate that CPADFN achieves better performances.
KW - Chinese phonetic alphabet
KW - Deep Fusion
KW - Extremely short comments
KW - Multi-head self-attention
KW - Sentiment classification
UR - https://www.scopus.com/pages/publications/85113774874
U2 - 10.1007/978-3-030-82147-0_46
DO - 10.1007/978-3-030-82147-0_46
M3 - 会议稿件
AN - SCOPUS:85113774874
SN - 9783030821463
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 562
EP - 576
BT - Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
A2 - Qiu, Han
A2 - Zhang, Cheng
A2 - Fei, Zongming
A2 - Qiu, Meikang
A2 - Kung, Sun-Yuan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 August 2021 through 16 August 2021
ER -