Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 5-8.

• Intelligent Computing • Previous Articles     Next Articles

Attribute Sentiment Classification Towards Question-answering Text

JIANG Ming-qi1, LEE Sophia Yat Mei2, LIU Huan1, LI Shou-shan1   

  1. (School of Computer Science & Technology,Soochow University,Suzhou 215006,China)1;
    (Department of Chinese and Bilingual Studies,Hong Kong Polytechnic University,Hong Kong 999077,China)2
  • Online:2019-11-10 Published:2019-11-20

PDF (PC)

884

Abstract: The goal of conditional sentiment analysis is getting the sentiment polarity of whole text,which is a coarsetask.Recently,with the improved technology,the sentiment analysis task is also refined,and the researchers hope to get sentiment polarity of given target of the text.This paper’s purpose is getting the sentiment polarity of product attribute on question-answering text.To perform attribute sentiment classification towards QA text pair,this paper proposed a novel approach based on attention mechanism.Firstly,this paper concatenated the attribute information on answer words’ vectors.Secondly,this paper leveraged LSTM models to encode the question text and answer text.Thirdly,this paper got the relation of question and answer by using attention mechanism and got the whole feature of answer.Finally,this paper got the result of whole feature by using classifier.Empirical studies demonstrate the effectiveness of the proposed approach to attribute sentiment classification towards question-answering text.

Key words: Attention mechanism, Question-answering text, Sentiment analysis

CLC Number: 

  • TP391
[1]GO A,BHAYANI R,HUANG L.Twitter sentiment classification using distant supervision:CS224N Project Report[R].Stanford,2009.
[2]MULLEN T,COLLIER N.Sentiment analysis using support vector machines with diverse information sources[C]∥Procee-dings of the 2004 Conference on Empirical Methods in Natural Language Processing.2004.
[3]KHAIRNAR J,KINIKAR M.Machine learning algorithms for opinion mining and sentiment classification[J].International Journal of Scientific and Research Publications,2013,3(6):1-6.
[4]唐晓波,刘广超.细粒度情感分析研究综述[J].图书情报工作,2017,61(5):132-140.
[5]BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Informationretrie-val,2009,12(5):526-558.
[6]JO Y,OH A H.Aspect and sentiment unification model for online review analysis[C]∥ACM International Conference on Web Search and Data Mining.ACM,2011:815-824.
[7]JIANG L,YU M,ZHOU M,et al.Target-dependent twitter sentiment classification[C]∥Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies-Volume 1.Association for Computational Linguistics,2011:151-160.
[8]LIU P,JOTY S,MENG H.Fine-grained opinion mining with recurrent neural networks and word embeddings[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1433-1443.
[9]TANG D,QIN B,LIU T.Aspect level sentiment classification with deep memory network[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:214-224.
[10]TANG D,QIN B,FENG X,et al.Effective LSTMs for target-dependent sentiment classification[C]∥Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics.2016.
[11]LAMBERT P.Aspect-level cross-lingual sentiment classification with constrained SMT[C]∥Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2:Short Papers).2015:781-787.
[12]MA D,LI S,ZHANG X,et al.Interactive attention networks for aspect-level sentiment classification[C]∥Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track.2017:4068-4074.
[13]HOCHREITER S,SCHMIDHUBER J.Flat minima[J].Neural Computation,1997,9(1):1- 42.
[14]GRAVES A.Generating Sequences With Recurrent Neural Networks [J].arXiv:1308.0850,2013.
[15]SOKOLOVA M,LAPALME G.A systematic analysis of performance measures for classification tasks[J].Information Processing & Management,2009,45(4):427-437.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[3] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[4] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[5] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[6] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[9] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[10] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[11] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[12] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[13] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[14] MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147.
[15] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.