A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term Sets

被引:12
作者
Liu, Zixu [1 ]
Liao, Huchang [2 ]
Li, Maolin [3 ,4 ]
Yang, Qian [2 ]
Meng, Fanlin [3 ,4 ]
机构
[1] Univ Southampton, Southampton Business Sch, Southampton SO17 1BJ, England
[2] Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China
[3] Univ Manchester, Dept Comp Sci, Manchester M13 9PL, England
[4] Univ Manchester, Alliance Manchester Business Sch, Manchester M139PL, England
基金
中国国家自然科学基金;
关键词
Feature extraction; Sentiment analysis; Training; Linguistics; Hidden Markov models; Support vector machines; Probabilistic logic; Deep learning (DL); probabilistic linguistic term set (PLTS); Index Terms; sentiment analysis; text classification; text reviews; CLASSIFICATION; MODEL; REVIEWS; ENSEMBLE; EXTRACTION; RATINGS; HOTELS;
D O I
10.1109/TEM.2023.3271597
中图分类号
F [经济];
学科分类号
02 ;
摘要
The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.
引用
收藏
页码:6677 / 6694
页数:18
相关论文
共 79 条
  • [1] Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews
    Al-Smadi, Mohammad
    Qawasmeh, Omar
    Al-Ayyoub, Mahmoud
    Jararweh, Yaser
    Gupta, Brij
    [J]. JOURNAL OF COMPUTATIONAL SCIENCE, 2018, 27 : 386 - 393
  • [2] [Anonymous], 2015, Advances in Neural Information Processing Systems
  • [3] How AI Helps to Increase Organizations' Capacity to Manage Complexity - A Research Perspective and Solution Approach Bridging Different Disciplines
    Azzam, Mark
    Beckmann, Rasmus
    [J]. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 2024, 71 : 2324 - 2337
  • [4] Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews
    Bi, Jian-Wu
    Liu, Yang
    Fan, Zhi-Ping
    Zhang, Jin
    [J]. TOURISM MANAGEMENT, 2019, 70 : 460 - 478
  • [5] Cai JJ, 2018, I COMP CONF WAVELET, P123, DOI 10.1109/ICCWAMTIP.2018.8632592
  • [6] XGBoost: A Scalable Tree Boosting System
    Chen, Tianqi
    Guestrin, Carlos
    [J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 785 - 794
  • [7] Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
    Chen, Yuxiao
    Yuan, Jianbo
    You, Quanzeng
    Luo, Jiebo
    [J]. PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), 2018, : 117 - 125
  • [8] Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
  • [9] Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
    Do, Hai Ha
    Prasad, P. W. C.
    Maag, Angelika
    Alsadoon, Abeer
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2019, 118 : 272 - 299
  • [10] Text Classification Research with Attention-based Recurrent Neural Networks
    Du, C.
    Huang, L.
    [J]. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2018, 13 (01) : 50 - 61