Abstract
Locating helpful reviewers in opinion-sharing communities is an important issue. Numerous studies that examine this using social relations have some shortcomings. This study investigates language use, differing from person to person, and develops a novel prediction model to alleviate the problems. We identify four stylistic aspects and explore their impacts on predicting reviewers’ helpfulness ratings. The analyses show that the proposed model can more accurately locate helpful reviewers than the baseline model. In addition, reviewers’ words impact more than social relations do, although a combination of these will boost prediction performance to a greater extent than one alone.
| Original language | English |
|---|---|
| Pages (from-to) | 28-38 |
| Number of pages | 11 |
| Journal | Information and Management |
| Volume | 56 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Management Information Systems
- Information Systems
- Information Systems and Management
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Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. / Li, Sheng-Tun; Pham, Thuong Thi; Chuang, Hui Chi.
In: Information and Management, Vol. 56, No. 1, 01.01.2019, p. 28-38.Research output: Contribution to journal › Article
TY - JOUR
T1 - Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles
AU - Li, Sheng-Tun
AU - Pham, Thuong Thi
AU - Chuang, Hui Chi
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Locating helpful reviewers in opinion-sharing communities is an important issue. Numerous studies that examine this using social relations have some shortcomings. This study investigates language use, differing from person to person, and develops a novel prediction model to alleviate the problems. We identify four stylistic aspects and explore their impacts on predicting reviewers’ helpfulness ratings. The analyses show that the proposed model can more accurately locate helpful reviewers than the baseline model. In addition, reviewers’ words impact more than social relations do, although a combination of these will boost prediction performance to a greater extent than one alone.
AB - Locating helpful reviewers in opinion-sharing communities is an important issue. Numerous studies that examine this using social relations have some shortcomings. This study investigates language use, differing from person to person, and develops a novel prediction model to alleviate the problems. We identify four stylistic aspects and explore their impacts on predicting reviewers’ helpfulness ratings. The analyses show that the proposed model can more accurately locate helpful reviewers than the baseline model. In addition, reviewers’ words impact more than social relations do, although a combination of these will boost prediction performance to a greater extent than one alone.
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UR - http://www.scopus.com/inward/citedby.url?scp=85048839474&partnerID=8YFLogxK
U2 - 10.1016/j.im.2018.06.002
DO - 10.1016/j.im.2018.06.002
M3 - Article
AN - SCOPUS:85048839474
VL - 56
SP - 28
EP - 38
JO - Information and Management
JF - Information and Management
SN - 0378-7206
IS - 1
ER -