A Hybrid HDP-ME-LDA Model for Sentiment Analysis

DOI

10.2991/amcce-17.2017.114How to use a DOI?

Keywords

Aspect Detection, Sentiment Analysis, LDA, Maximum Entropy, Hierarchical Dirichlet Process

Abstract

Sentiment Analysis is an important research area in data mining. Recently, various topic models for aspect detection and sentiment analysis have been proposed, many of which are based on Latent Dirichlet Allocation(LDA) unsupervised machine learning approach. LDA requires the number of topics in advance which are often based on unreliable experience for different areas. On the other hand, it is important to identify aspect and opinion words of topics, especially aspect-specific opinion words and analyze sentiment polarity. But few research did them all. To solve these problems, this paper proposes a hybrid Hierarchical Dirichlet Process and Maximum Entropy-Latent Dirichlet Allocation(HDP-ME-LDA) model. It uses HDP to automatically determine the number of topics and utilizes maximum entropy classifier to separate aspect and opinion words, including aspect-specific opinion words. We evaluate our model on data sets of reviews of restaurant and electronic devices qualitatively and quantitatively. The result shows that we perform better than other topic models, like JST, ASUM, MaxEnt-LDA, HDP-LDA.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Yuan
AU  - Gang Wu
PY  - 2017/03
DA  - 2017/03
TI  - A Hybrid HDP-ME-LDA Model for Sentiment Analysis
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 659
EP  - 663
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.114
DO  - 10.2991/amcce-17.2017.114
ID  - Yuan2017/03
ER  -