کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6854672 1437592 2018 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews
ترجمه فارسی عنوان
یک مدل احتمال احتمالی مشترک برای نظارت فریبنده در مورد موضوع نظارت بی نظیر
کلمات کلیدی
تشخیص بررسی فریبنده، مدل احتمال احتمالی مشترک موضوع تخصیص نامحدود تابعه، نمونه برداری گیبس،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In electronic commerce, online reviews play very important roles in customers' purchasing decisions. Unfortunately, malicious sellers often hire buyers to fabricate fake reviews to improve their reputation. In order to detect deceptive reviews and mine the topics and sentiments from the reviews, in this paper, we propose an unsupervised topic-sentiment joint probabilistic model (UTSJ) based on Latent Dirichlet Allocation (LDA) model. This model first employs Gibbs sampling algorithm to approximate parameters of maximum likelihood function offline and obtain topic-sentiment joint probabilistic distribution vector for each review. Secondly, a Random Forest classifier and a SVM (Support Vector Machine) classifier are trained offline, respectively. Experimental results on real-life datasets show that our proposed model is better than baseline models such as n-grams, character n-grams in token, POS (part-of-speech), LDA, and JST (Joint Sentiment/Topic). Moreover, our UTSJ model outperforms or performs similarly to benchmark models in detecting deceptive reviews over balanced dataset and unbalanced dataset in different domains. Particularly, our UTSJ model is good at dealing with real-life unbalanced big data, which makes it very suitable for being applied in e-commerce environment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 114, 30 December 2018, Pages 210-223
نویسندگان
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