Ajou University repository

Detecting negative deceptive opinion from tweets
Citations

SCOPUS

0

Citation Export

Publication Year
2018-01-01
Journal
Lecture Notes in Electrical Engineering
Publisher
Springer Verlag
Citation
Lecture Notes in Electrical Engineering, Vol.425, pp.329-339
Keyword
Lexical featuresNegative deceptive opinionOpinion miningPersonal profile and behavioral featuresPositive deceptive opinionTweet
Mesh Keyword
Behavioral featuresLexical featuresNegative deceptive opinionOpinion miningPositive deceptive opinionTweet
All Science Classification Codes (ASJC)
Industrial and Manufacturing Engineering
Abstract
Nowadays, a huge amount of opinions about specific brands of a company are shared on the Web. Such opinions are an important source of information for customers and companies. Unfortunately, there is an increasing number of deceptive opinions in order to deceive consumers by promoting a low quality product (positive deceptive) or by criticizing a potentially better quality product (negative deceptive). This paper focuses on the detection of negative deceptive opinions from tweets on specific brands of a company. We developed a classifier that detects negative deceptive opinions by combining lexical features of a tweet and personal profile and behavioural features of the writer. One of the challenges to develop this system is the lack of labeled dataset for training and testing. To resolve this issue, we collect our own dataset and label each tweet by multiple experts. Our experimental results show that the proposed system is a promising approach for detecting negative deceptive opinions. Our approach can help to identify defamers by analyzing personal profiles and writing style of each writer.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36341
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022211633&origin=inward
DOI
https://doi.org/2-s2.0-85022211633
Journal URL
http://www.springer.com/series/7818
Type
Conference
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2016R1D1A1B03933875].
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.