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Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels [PDF]

, 2017
In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ...
Mohamad Hardyman Barawi, Chenghua Lin 0002, Advaith Siddharthan   +2 more
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A Survey of Multi-Label Topic Models

ACM SIGKDD Explorations Newsletter, 2019
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications and fiction. To keep track of this data, there are categories, key words, tags or labels that are assigned to each text.
Sophie Burkhardt, Stefan Kramer 0001
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Topic labeled text classification

Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014
Supervised text classifiers require extensive human expertise and labeling efforts. In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics. Our algorithm is based on the generative property of LDA.
Swapnil Hingmire, Sutanu Chakraborti
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Using Topic Labels for Text Summarization

2017
Multi-document summarization is a difficult natural language processing task. Many extractive summarization methods consist of two steps: extract important concepts of documents and select sentences based on those concepts. In this paper, we introduce a method to use the Latent Dirichlet Allocation (LDA) topic labels as concepts, instead of n-gram or ...
Wanqiu Kou, Fang Li, Zhe Ye
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Time Label Topic Model

International Journal of Database Theory and Application, 2015
Most of the models not aware of these dependencies on document time stamps. Not modeling time can confound co-occurrence patters and results in exchangeability of topic problem, which is important factor to deal with when finding dynamic topic discovery.
Yong Heng Chen, Wanli Zuo, Kerui Chen, Yaojin Lin   +3 more
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Multi-Label Topic Model Conditioned on Label Embedding

2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI), 2019
In most real-world document collections, there are various types of labels that usually carry context information, such as label hierarchies or textual descriptions. Nonetheless, the commonly-used approaches to modeling text corpora ignore this information.
Lin Tang, Lin Liu, Jianhou Gan
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Finding High-Level Topics and Tweet Labeling Using Topic Models

2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), 2015
Making sense of Twitter data streams is challenging due to the extremely high volume of data. One way to address this challenge is to consider these data streams as containing a set of high-level topics. In this research we address the problem of: given a collection of tweets about K high-level topics, how to find topic words that describe these topics
Sameendra Samarawickrama, Shanika Karunasekera, Aaron Harwood   +2 more
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Supervised topic models for multi-label classification

Neurocomputing, 2015
Recently, some publications indicated that the generative modeling approaches, i.e., topic models, achieved appreciated performance on multi-label classification, especially for skewed data sets. In this paper, we develop two supervised topic models for multi-label classification problems.
Ximing Li 0002, Jihong Ouyang, Xiaotang Zhou   +2 more
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Web Search Clustering and Labeling with Hidden Topics

ACM Transactions on Asian Language Information Processing, 2009
Web search clustering is a solution to reorganize search results (also called “snippets”) in a more convenient way for browsing. There are three key requirements for such post-retrieval clustering systems: (1) the clustering algorithm should group similar documents together; (2) clusters should be labeled with descriptive phrases; and (3) the ...
Cam-Tu Nguyen, Xuan Hieu Phan, Susumu Horiguchi, Thu-Trang Nguyen, Quang-Thuy Ha   +4 more
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Using Topic Models to Label Documents for Classification

2020
Document classifiers are supervised learning models in which documents are assigned categories based on models that are trained on annotated datasets. In this paper, we use topic models to automatically assign categories to documents, which later are fed to document classification models.
Khang Nhut Lam, Lam Thanh Truong, Jugal Kalita   +2 more
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