A Novel Hybrid HDP-LDA Model for Sentiment Analysis

Ding, Wanying; Song, Xiaoli; Guo, Lifan; Xiong, Zunyan; Hu, Xiaohua

doi:10.1109/wi-iat.2013.47

Cited by 13 publications

(13 citation statements)

References 14 publications

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“…The other disadvantage is that it does not classify sentiment polarity. To automatically generate the number of topics, HDP-LDA is purposed in [2]. HDP is a nonparametric Bayesian model which replaces Dirichlet allocation with Dirichlet processes.…”

Section: Related Workmentioning

confidence: 99%

“…A j,I is generated by a table distribution G j in CRP. Whether A j,i is a new table or an existing table is up to G j [2]. G j draws from a global distribution G 0 , a CRP dish distribution.…”

Section: Advances In Engineering Research Volume 118mentioning

confidence: 99%

“…Fourth, LDA model doesn't have context information. This paper combines HDP-LDA [2] with MaxEnt-LDA [3] and improves MaxEnt-LDA, finally proposes HDP-ME-LDA model to solve these problems. Firstly, we divide sentences or reviews into "clause".…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid HDP-ME-LDA Model for Sentiment Analysis

Yuan¹,

Wu²

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Advances In Engineering Research Volume 118mentioning

confidence: 99%

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A Hybrid HDP-ME-LDA Model for Sentiment Analysis

Yuan¹,

Wu²

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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“…The JST model makes a 4-layer probability model, which gets the sentiment distribution of each topic by obtaining the correspondence between statistical labels and label sentiment theme. In 2013, Ding et al [9]. proposed HDP-LDA (Hierarchical Dirichlet Process-Latent Dirichlet Allocation) model.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tourism Micro-blog Comments

Song¹,

Wang²

2017

dtcse

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Abstract. Recent years, an amount of tourism micro-blog comments on the Internet have become an important source of information for potential customers and to improve the service quality. These micro-blog comments do help to research tourism resources or services before making decisions. Thus, sentiment analysis of tourism micro-blog comments has become a hot issue in the field of natural language processing and text mining. We designed a system called SASTMC by using web crawler, Chinese words segmentation, emotion words dictionary and an improved TF-IDF algorithm. It enhances expression ability of sentiment information of text words. Experiments on Sina micro-blog comments datasets demonstrate that our method can do the task well.

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“…For this reason, the 5Vs theme in big data is revisited. Several literatures have started to explore the big data issue for SA, such as for the scalability issue (Bing and Chan, 2014;Conejero et al, 2013;Liu et al, 2013), introduction of big data tools for SA (Ding et al, 2013;Mihanović et al, 2014;Prom-on et al, 2014), distributed approach for SA processing (Bravo-Marquez et al, 2014;Fulse et al, 2014;Hossein and Rahnama, 2014) and improved ML models for SA on big data (Bing and Chan, 2014;Ding et al, 2013;Liu et al, 2013;Mukkamala et al, 2014). Undoubtedly, these papers are dated around the year 2014, which marks the booming of the big data era.…”

Section: Gaps and Opportunities Between Sentiment Analysis Approachesmentioning

confidence: 99%

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Sharef¹,

Zin²,

Nadali³

2016

Journal of Computer Science

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Abstract:The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.

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A Novel Hybrid HDP-LDA Model for Sentiment Analysis

Cited by 13 publications

References 14 publications

A Hybrid HDP-ME-LDA Model for Sentiment Analysis

A Hybrid HDP-ME-LDA Model for Sentiment Analysis

Sentiment Analysis of Tourism Micro-blog Comments

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

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