Aspects Extraction for Aspect Level Opinion Analysis Based on Deep CNN

被引:2
|
作者
Pour, Ali Alemi Matin [1 ]
Jalili, Saeed [1 ]
机构
[1] Tarbiat Modares Univ, Comp Engn Dept, Tehran, Iran
关键词
aspect extraction; opinion analysis; deep CNN; natural language processing; deep learning; neural network;
D O I
10.1109/CSICC52343.2021.9420630
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Extracting aspect term is essential for aspect level sentiment analysis; Sentiment analysis collects and extracts the opinions expressed in social media and websites' comments and then analyzes them, helping users and stakeholders understand public views on the issues raised better and more quickly. Aspect-level sentiment analysis provides more detailed information, which is very beneficial for use in many various domains. In this paper, the significant contribution is to provide a data preprocessing method and a deep convolutional neural network (CNN) to label each word in opinionated sentences as an aspect or non-aspect word. The proposed method extracts the terms of the aspect that can be used in analyzing the sentiment of the expressed aspect terms in the comments and opinions. The experimental results of the proposed method performed on the SemEval-2014 dataset show that it performs better than other prominent methods such as deep CNN. The proposed data preprocessing method with the deep CNN network can improve extraction of aspect terms according to F-measure by at least 1.05% and 0.95% on restaurant and laptop domains.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Automated Rule Selection for Aspect Extraction in Opinion Mining
    Liu, Qian
    Gao, Zhiqiang
    Liu, Bing
    Zhang, Yuanlin
    PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 1291 - 1297
  • [32] Cross-Language Aspect Extraction for Opinion Mining
    Nguyen Thi Thanh Thuy
    Ngo Xuan Bach
    Tu Minh Phuong
    PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (KSE), 2018, : 67 - 72
  • [33] A Logic Programming Approach to Aspect Extraction in Opinion Mining
    Liu, Qian
    Gao, Zhiqiang
    Liu, Bing
    Zhang, Yuanlin
    2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, 2013, : 276 - 283
  • [34] Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction
    Zhang, Yongsheng
    Ding, Qi
    Zhu, Zhenfang
    Liu, Peiyu
    Xie, Fu
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2022, 59 (02) : 523 - 542
  • [35] Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction
    Yongsheng Zhang
    Qi Ding
    Zhenfang Zhu
    Peiyu Liu
    Fu Xie
    Journal of Intelligent Information Systems, 2022, 59 : 523 - 542
  • [36] Span-based relational graph transformer network for aspect–opinion pair extraction
    You Li
    Chaoqiang Wang
    Yuming Lin
    Yongdong Lin
    Liang Chang
    Knowledge and Information Systems, 2022, 64 : 1305 - 1322
  • [37] Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
    Klein, Ayal
    Pereg, Oren
    Korat, Daniel
    Lal, Vasudev
    Wasserblat, Moshe
    Dagan, Ido
    PROCEEDINGS OF THE 12TH WORKSHOP ON COMPUTATIONAL APPROACHES TO SUBJECTIVITY, SENTIMENT & SOCIAL MEDIA ANALYSIS, 2022, : 104 - 112
  • [38] CABiLSTM-BERT: Aspect-based sentiment analysis model based on deep implicit feature extraction
    He, Bo
    Zhao, Ruoyu
    Tang, Dali
    KNOWLEDGE-BASED SYSTEMS, 2025, 309
  • [39] Facial Feature Extraction Method Based on Shallow and Deep Fusion CNN
    Liang, Xiaoxi
    Cai, Xiaodong
    Li, Longze
    Chen, Yun
    2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2017, : 50 - 53
  • [40] Deep Texture Exemplar Extraction Based on Trimmed T-CNN
    Wu, Huisi
    Yan, Wei
    Li, Ping
    Wen, Zhenkun
    IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 23 : 4502 - 4514