TY - GEN
T1 - Sentiment analysis based on bi-LSTM using tone
AU - Li, Huakang
AU - Wang, Lei
AU - Wang, Yongchao
AU - Sun, Guozi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In view of most of sentiment analysis texts are too short to get enough textual features, a method of bidirectional Long Short-Term Memory using tone (Word, Character and Tone model based on Bidirectional Long Short-Term Memory, WCT-Bi-LSTM) was proposed. Distinguished from the general method of sentiment analysis only taking word as the feature, the model also used character and tone features as input to enrich the characteristics of the text. After that, the model integrated the deep semantic meaning of word, character and tone. It could better grasp the emotion of the text and improve the accuracy of sentiment classification. The experimental results show that, compared with the model which does not integrate tone, the accuracy of the proposed model is increased by 1.2% and 0.9%on two experimental datasets respectively, which proves that the proposed method can effectively improve the accuracy of sentiment classification.
AB - In view of most of sentiment analysis texts are too short to get enough textual features, a method of bidirectional Long Short-Term Memory using tone (Word, Character and Tone model based on Bidirectional Long Short-Term Memory, WCT-Bi-LSTM) was proposed. Distinguished from the general method of sentiment analysis only taking word as the feature, the model also used character and tone features as input to enrich the characteristics of the text. After that, the model integrated the deep semantic meaning of word, character and tone. It could better grasp the emotion of the text and improve the accuracy of sentiment classification. The experimental results show that, compared with the model which does not integrate tone, the accuracy of the proposed model is increased by 1.2% and 0.9%on two experimental datasets respectively, which proves that the proposed method can effectively improve the accuracy of sentiment classification.
KW - Bidirectional long short-term memory (Bi LSTM)
KW - Character embedding
KW - Sentiment analysis
KW - Tone
KW - Word embedding
UR - http://www.scopus.com/inward/record.url?scp=85083231034&partnerID=8YFLogxK
U2 - 10.1109/SKG49510.2019.00014
DO - 10.1109/SKG49510.2019.00014
M3 - Conference Proceeding
AN - SCOPUS:85083231034
T3 - Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019
SP - 30
EP - 35
BT - Proceedings - 15th International Conference on Semantics, Knowledge and Grids
A2 - Zhuge, Hai
A2 - Sun, Xiaoping
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Semantics, Knowledge and Grids, SKG 2019
Y2 - 17 September 2019 through 18 September 2019
ER -