Classifying Sentiment Based on LDA Model

Qing Xi Peng

doi:10.4028/www.scientific.net/AMM.427-429.2614

Paper Titles

A Dynamic Task Scheduling Strategy Based on MVFSA in Cloud Computing Environment
p.2596

Summarize the Security Issues of Internet of Things and Prospect of Future Trends
p.2600

Research on the Structure of Smart Laboratory Based on the Internet of Things Technology
p.2605

Study on Group Decision Support System for Production Resource Allocation of Engineering Machinery
p.2609

Classifying Sentiment Based on LDA Model
p.2614

Near Duplicated Text Detection Based on MapReduce
p.2618

An Improved Backoff Scheme for the IEEE 802.15.4 MAC Protocol
p.2622

Research of Security Strategy of Cloud Storage
p.2626

The Data Fusion Survivability Analysis Technology of Wireless Sensor Network
p.2630

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429Classifying Sentiment Based on LDA Model

Classifying Sentiment Based on LDA Model

Abstract:

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

2614-2617

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.2614

Citation:

Cite this paper

Online since:

September 2013

Authors:

Qing Xi Peng*

Keywords:

Gibbs Sampling, Latent Dirichlet Allocation (LDA), Sentiment Classification, Topic Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Blei D, NG A Y, and Jordan M I: Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003. 3(3): 993-1022.

Google Scholar

[2] Griffiths T, Stevyers M: Finding Scientific Topics. Proceedings of the Natural Academy of Sciences 2004. 101: 5528-5535.

Google Scholar

[3] Hongning Wang, Yue Lu and ChengXiang Zhai: Latent Aspect Rating Analysis without Aspect Keyword Supervision. KDD'2011, 618-626.

DOI: 10.1145/2020408.2020505

Google Scholar

[4] Xu Ge, Wang Hou-Feng: The Development of Topic Models in Natural Language Processing, Chinese Journal of Computers 34(8): 1423-1436 October (2011).

DOI: 10.3724/sp.j.1016.2011.01423

Google Scholar

[5] Lin, Chenghua, et al. : Weakly supervised joint sentiment-topic detection from text, Knowledge and Data Engineering, IEEE Transactions on 24. 6 (2012): 1134-1145.

DOI: 10.1109/tkde.2011.48

Google Scholar

[6] Moghaddam, Samaneh, and Martin Ester. ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews., Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, (2011).

DOI: 10.1145/2009916.2010006

Google Scholar