Modeling topic hierarchies with the recursive chinese restaurant process

Kim, Joon Hee; Kim, Dongwoo; Kim, Suin; Oh, Alice

doi:10.1145/2396761.2396861

Cited by 51 publications

(66 citation statements)

References 18 publications

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“…To overcome this limitation, USTM framework includes non-parametric topic models based on Dirichlet Process. The work that is the closest to ours is [10] Hierarchical Aspect Sentiment Unification Model (HASUM), which extends the ASUM model by integrating it with the recursive Chinese Restaurant Process [9], a modified version of the nested Chinese Restaurant Process [1], thus allowing to identify hierarchical aspect-sentiment structure. However, HASUM does not consider user meta-data.…”

Section: Related Workmentioning

confidence: 99%

Parametric and Non-parametric User-aware Sentiment Topic Models

Yang

Kotov

Mohan

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many online consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.

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Section: Related Workmentioning

confidence: 99%

Parametric and Non-parametric User-aware Sentiment Topic Models

Yang

Kotov

Mohan

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

show abstract

“…Instead of having a pool of flat topics, these models assume an internal hierarchical structure of the topics. Different models use different generative processes to simulate this hierarchical structure, such as nested Chinese Restaurant Process [10], Pachinko Allocation [17], hierarchical Pachinko Allocation [20], recursive Chinese Restaurant Process [14], and nested Chinese Restaurant Franchise [2]. When these models are applied to constructing a topical hierarchy, the entire hierarchy is inferred all at once from the corpus.…”

Section: Related Workmentioning

confidence: 99%

“…Most existing hierarchical topic modeling techniques [10, 17, 20, 14, 2] are based on the extensions of latent Dirichlet allocation (LDA), and are not designed for interactive construction of the hierarchy. First, the inference algorithms for these models are expensive, demanding hundreds or thousands of passes of data.…”

Section: Introductionmentioning

confidence: 99%

Towards Interactive Construction of Topical Hierarchy

Wang

Liu

Song

2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Automatic construction of user-desired topical hierarchies over large volumes of text data is a highly desirable but challenging task. This study proposes to give users freedom to construct topical hierarchies via interactive operations such as expanding a branch and merging several branches. Existing hierarchical topic modeling techniques are inadequate for this purpose because (1) they cannot consistently preserve the topics when the hierarchy structure is modified; and (2) the slow inference prevents swift response to user requests. In this study, we propose a novel method, called STROD, that allows efficient and consistent modification of topic hierarchies, based on a recursive generative model and a scalable tensor decomposition inference algorithm with theoretical performance guarantee. Empirical evaluation shows that STROD reduces the runtime of construction by several orders of magnitude, while generating consistent and quality hierarchies.

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“…Restaurant Process [48], and nested Chinese Resturant Franchise [4]. When these models are applied to constructing a topical hierarchy, the entire hierarchy must be inferred all at once from the corpus.…”

Section: Hierarchical Topic Modelsmentioning

confidence: 99%

Mining latent entity structures from massive unstructured and interconnected data

Wang

2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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The "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured but interconnected data. Mining latent structured information around entities uncovers semantic structures from massive unstructured data and hence enables many high-impact applications, including taxonomy or knowledge base construction, multi-dimensional data analysis and information or social network analysis.A mining framework is proposed, to solve and integrate a chain of tasks: hierarchical topic discovery, topical phrase mining, entity role analysis and entity relation mining. It reveals two main forms of structures: topical and relational structures. The topical structure summarizes the topics associated with entities with various granularity, such as the research areas in computer science. The framework enables recursive construction of phrase-represented and entity-enriched topic hierarchy from text-attached information networks. It makes breakthrough in terms of quality and computational efficiency. The relational structure recovers the hidden relationship among entities, such as advisor-advisee. A probabilistic graphical modeling approach is proposed. The method can utilize heterogeneous attributes and links to capture all kinds of semantic signals, including constraints and dependencies, to recover the hierarchical relationship with the best known accuracy.ii

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Modeling topic hierarchies with the recursive chinese restaurant process

Cited by 51 publications

References 18 publications

Parametric and Non-parametric User-aware Sentiment Topic Models

Parametric and Non-parametric User-aware Sentiment Topic Models

Towards Interactive Construction of Topical Hierarchy

Mining latent entity structures from massive unstructured and interconnected data

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