A concept-level approach to the analysis of online review helpfulness

被引:130
|
作者
Qazi, Aika [1 ,6 ]
Syed, Karim Bux Shah [2 ,7 ]
Raj, Ram Gopal [1 ]
Cambria, Erik [3 ]
Tahir, Muhammad [4 ]
Alghazzawi, Daniyal [5 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia
[2] Univ Malaya, Fac Business & Accountancy, Kuala Lumpur 50603, Malaysia
[3] Nanyang Technol Univ, Sch Comp Engn, 50 Nanyang Ave, Singapore, Singapore
[4] Univ Jeddah, Fac Comp & Informat Technol, Jeddah, Saudi Arabia
[5] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21413, Saudi Arabia
[6] COMSATS Inst Informat Technol, Fac Comp Sci & Informat Technol, Islamabad, Pakistan
[7] Univ Sindh, Inst Business Adm, Jamshoro 76080, Pakistan
关键词
Online reviews; Review helpfulness; Electronic commerce; Suggestive reviews; CUSTOMER REVIEWS; PRODUCT REVIEWS; INFORMATION; DETERMINANTS; SALES; MODEL;
D O I
10.1016/j.chb.2015.12.028
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Helpfulness of online reviews serves multiple needs of different Web users. Several types of factors can drive reviews' helpfulness. This study focuses on uninvestigated factors by looking at not just the quantitative factors (such as the number of concepts), but also qualitative aspects of reviewers (including review types such as the regular, comparative and suggestive reviews and reviewer helpfulness) and builds a conceptual model for helpfulness prediction. The set of 1500 reviews were randomly collected from TripAdvisor.com across multiple hotels for analysis. A set of four hypotheses were used to test the proposed model. Our results suggest that the number of concepts contained in a review, the average number of concepts per sentence, and the review type contribute to the perceived helpfulness of online reviews. The regular reviews were not statistically significant predictors of helpfulness. As a result, review types and concepts have a varying degree of impact on review helpfulness. The findings of this study can provide new insights to e-commerce retailers in understanding the importance of helpfulness of reviews. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:75 / 81
页数:7
相关论文
共 50 条
  • [1] PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis
    Satapathy, Ranjan
    Singh, Aalind
    Cambria, Erik
    COMPUTATIONAL DATA AND SOCIAL NETWORKS, 2019, 11917 : 177 - 188
  • [2] A Fuzzy System for Concept-Level Sentiment Analysis
    Dragoni, Mauro
    Tettamanzi, Andrea G. B.
    Pereira, Celia da Costa
    SEMANTIC WEB EVALUATION CHALLENGE, 2014, 475 : 21 - 27
  • [3] A Concept-Level Approach in Analyzing Review Readership for E-Commerce Persuasive Recommendation
    Lah, Nur Syadhila Bt Che
    Hussin, Ab Razak Bin Che
    Dahlan, Mohamed
    2017 5TH INTERNATIONAL CONFERENCE ON RESEARCH AND INNOVATION IN INFORMATION SYSTEMS (ICRIIS 2017): SOCIAL TRANSFORMATION THROUGH DATA SCIENCE, 2017,
  • [4] ESWC'14 Challenge on Concept-Level Sentiment Analysis
    Recupero, Diego Reforgiato
    Cambria, Erik
    SEMANTIC WEB EVALUATION CHALLENGE, 2014, 475 : 3 - 20
  • [5] FinSenticNet: A Concept-Level Lexicon for Financial Sentiment Analysis
    Du, Kelvin
    Xing, Frank
    Mao, Rui
    Cambria, Erik
    2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, 2023, : 109 - 114
  • [6] Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach
    Agarwal, Basant
    Poria, Soujanya
    Mittal, Namita
    Gelbukh, Alexander
    Hussain, Amir
    COGNITIVE COMPUTATION, 2015, 7 (04) : 487 - 499
  • [7] Statistical Approaches to Concept-Level Sentiment Analysis Introduction
    Cambria, Erik
    Schuller, Bjoern
    Liu, Bing
    Wang, Haixun
    Havasi, Catherine
    IEEE INTELLIGENT SYSTEMS, 2013, 28 (03) : 6 - 9
  • [8] Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach
    Basant Agarwal
    Soujanya Poria
    Namita Mittal
    Alexander Gelbukh
    Amir Hussain
    Cognitive Computation, 2015, 7 : 487 - 499
  • [9] ESWC 15 Challenge on Concept-Level Sentiment Analysis
    Recupero, Diego Reforgiato
    Dragoni, Mauro
    Presutti, Valentina
    SEMANTIC WEB EVALUATION CHALLENGES, 2015, 548 : 211 - 222
  • [10] Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish
    Bajpai, Rajiv
    Ho, Danyun
    Cambria, Erik
    COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, (CICLING 2016), PT II, 2018, 9624 : 347 - 361