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Author Wanying Ding ♦ Xiaoli Song ♦ Lifan Guo ♦ Zunyan Xiong ♦ Xiaohua Hu
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Context ♦ Latent Dirichlet Allocation ♦ Adaptation models ♦ Analytical models ♦ Dictionaries ♦ Aspect Detection ♦ Educational institutions ♦ Probabilistic logic ♦ Hierarchical Dirichlet Process ♦ Sentiment Analysis ♦ Resource management ♦ Probabilistic Model
Abstract Sentiment analysis studies the public opinions towards an entity, and it is an important research area in data mining. Recently, a lot of sentiment analysis models have been proposed, including supervised and unsupervised approaches. However, the role of supervised models has been undermined by the phenomenon of big data, and the unsupervised ones are drawing more and more attention. But, most current unsupervised methods are based on Latent Dirichlet Allocation (LDA), and they need to specify the number of aspects in advance, making them subjective. In addition, these methods treat factual words and opinioned words the same, and assume that one sentence contains only one aspect, all of which make the existing unsupervised methods unsatisfactory. To solve these problems, this paper proposes a novel hybrid Hierarchical Dirichlet Process-Latent Dirichlet Allocation (HDP-LDA) model. This model can automatically determine the number of aspects, distinguish factual words from opinioned words, and further effectively extracts the aspect specific sentiment words. Experiment result shows that our model can clearly capture the aspects people mentioned and the specific sentiment words they use in each aspect, improving the performance of sentiment analysis efficiently. At last, we compared our model with the influential topic models, namely, JST, AUSM and Maxine-LDA, on the online restaurant review, and found our model performs very well.
Description Author affiliation: Coll. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA (Wanying Ding; Xiaoli Song; Lifan Guo; Zunyan Xiong; Xiaohua Hu)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-11-17
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9780769551456
Size (in Bytes) 547.59 kB
Page Count 8
Starting Page 329
Ending Page 336

Source: IEEE Xplore Digital Library