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
  • [21] Aspect Extraction for Opinion Mining with a Semantic Model
    Henriquez, Carlos
    Sanchez-Torres, German
    ENGINEERING LETTERS, 2021, 29 (01) : 61 - 67
  • [22] Intelligent fake reviews detection based on aspect extraction and analysis using deep learning
    Bathla, Gourav
    Singh, Pardeep
    Singh, Rahul Kumar
    Cambria, Erik
    Tiwari, Rajeev
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (22): : 20213 - 20229
  • [23] Intelligent fake reviews detection based on aspect extraction and analysis using deep learning
    Gourav Bathla
    Pardeep Singh
    Rahul Kumar Singh
    Erik Cambria
    Rajeev Tiwari
    Neural Computing and Applications, 2022, 34 : 20213 - 20229
  • [24] Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations
    Liu, Qian
    Liu, Bing
    Zhang, Yuanlin
    Kim, Doo Soon
    Gao, Zhiqiang
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 2986 - 2992
  • [25] SAE : Syntactic-based Aspect and Opinion Extraction from Product Reviews
    Maharani, Warih
    Widyantoro, Dwi H.
    Khodra, Masayu L.
    2015 2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATICS: CONCEPTS, THEORY AND APPLICATIONS ICAICTA, 2015,
  • [26] Rule-based Opinion Target and Aspect Extraction to Acquire Affective Knowledge
    Gindl, Stefan
    Weichselbraun, Albert
    Scharl, Arno
    PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), 2013, : 557 - 563
  • [27] The impact of semantics on aspect level opinion mining
    Aboelela E.M.
    Gad W.
    Ismail R.
    PeerJ Computer Science, 2021, 7 : 1 - 22
  • [28] The impact of semantics on aspect level opinion mining
    Aboelela, Eman M.
    Gad, Walaa
    Ismail, Rasha
    PEERJ COMPUTER SCIENCE, 2021,
  • [29] Opinion Tree Parsing for Aspect-based Sentiment Analysis
    Bao, Xiaoyi
    Jiang, Xiaotong
    Wang, Zhongqing
    Zhang, Yue
    Zhou, Guodong
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023), 2023, : 7971 - 7984
  • [30] Aspect-based Sentiment Analysis with Opinion Tree Generation
    Bao, Xiaoyi
    Wang Zhongqing
    Jiang, Xiaotong
    Xiao, Rong
    Li, Shoushan
    PROCEEDINGS OF THE THIRTY-FIRST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2022, 2022, : 4044 - 4050