Sentiment analysis from travellers' reviews using enhanced conjunction rule based approach for feature-specific evaluation of hotels

被引:4
|
作者
Maity, Aranyak [1 ]
Ghosh, Sritama [1 ]
Karfa, Saikat [1 ]
Mukhopadhyay, Moutan [1 ]
Pal, Saurabh [1 ]
Pramanik, Pijush Kanti Dutta [2 ]
机构
[1] Bengal Inst Technol, Dept Comp Sci & Engn, Kolkata 700150, W Bengal, India
[2] Natl Inst Technol, Dept Comp Sci & Engn, Durgapur 713209, W Bengal, India
来源
关键词
Sentiment analysis; Opinion mining; Online review; Aspect-based opinion mining; Aspect-based sentiment analysis; Machine learning; Sentiment orientation; Tourism reviews; Lexicon;
D O I
10.1080/09720510.2020.1799499
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The evolution of the internet has steered an enormous amount of travel reviews of hotels on the web. People referring to these reviews are often overloaded and confused by the sheer amount of information available. Sentiment analysis techniques have been successful in aggregating the reviews, extracting their sentiments and thereby minimizing the information overload. But lacking in specific feature-based sentiment analysis has restricted customers in getting the actual scenario of hotels entirely. This paper presents a prospective design on lexicon-based approach for feature-based sentiment analysis of travel reviews on hotels or resorts. In particular, an enhanced form of conjuncture-based approach is proposed to segregate sentences into relevant clauses, identifying the feature and the sentiment value associated with it. Overall sentiment score for features like food, service, and location of a hotel is being calculated. The experiment results show significantly better accuracy and precision than the conventional text segregation and sentiment analysis methods, namely trigram and conjunction rule based approach.
引用
收藏
页码:983 / 997
页数:15
相关论文
共 50 条
  • [31] LMS Content Evaluation System with Sentiment Analysis Using Lexicon-Based Approach
    Tan, Riegie D.
    Piad, Keno
    Lagman, Ace
    Victoriano, Jayson
    Tano, Isagani
    San Gabriel, Nicanor, Jr.
    Espino, Joseph
    2022 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND EDUCATION TECHNOLOGY (ICIET 2022), 2022, : 93 - 98
  • [32] A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis
    Kaur, Gagandeep
    Sharma, Amit
    JOURNAL OF BIG DATA, 2023, 10 (01)
  • [33] A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis
    Gagandeep Kaur
    Amit Sharma
    Journal of Big Data, 10
  • [34] Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach
    Nasfi, Rim
    Bouguila, Nizar
    STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, S+SSPR 2022, 2022, 13813 : 74 - 83
  • [35] Extracting Insights From Competitor's Mistakes: A Sentiment Analysis Approach Using Competitive set Online Reviews
    Chalupa, Stepan
    Petricek, Martin
    Chadt, Karel
    TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS, 2023, 12 (03): : 1754 - 1761
  • [36] Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis
    Zhang, Wenhao
    Xu, Hua
    Wan, Wei
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (11) : 10283 - 10291
  • [37] Sentiment Analysis for Assessment of Hotel Services Review using Feature Selection Approach based-on Decision Tree
    Apriliani, Dyah
    Abidin, Taufiq
    Sutanta, Edhy
    Hamzah, Amir
    Somantri, Oman
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (04) : 240 - 245
  • [38] OBTAINING FEATURE- AND SENTIMENT-BASED LINKED INSTANCE RDF DATA FROM UNSTRUCTURED REVIEWS USING ONTOLOGY-BASED MACHINE LEARNING
    Santosh, D. Teja
    Vardhan, B. Vishnu
    INTERNATIONAL JOURNAL OF TECHNOLOGY, 2015, 6 (02) : 198 - +
  • [39] DeepMetaGen: an unsupervised deep neural approach to generate template-based meta-reviews leveraging on aspect category and sentiment analysis from peer reviews
    Sandeep Kumar
    Tirthankar Ghosal
    Asif Ekbal
    International Journal on Digital Libraries, 2023, 24 : 263 - 281
  • [40] DeepMetaGen: an unsupervised deep neural approach to generate template-based meta-reviews leveraging on aspect category and sentiment analysis from peer reviews
    Kumar, Sandeep
    Ghosal, Tirthankar
    Ekbal, Asif
    INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES, 2023, 24 (04) : 263 - 281