A semantic modeling method for social network short text based on spatial and temporal characteristics

Kou, Feifei; Du, Junping; Lin, Zijian; Liang, Meiyu; Li, Haisheng; Shi, Lei; Yang, Congxian

doi:10.1016/j.jocs.2017.10.012

Cited by 31 publications

(15 citation statements)

References 33 publications

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“…Other contextual auxiliary information in short texts has been used for aggregation such as hashtags, locations, and named entities [1]- [3], [11]. Kou et al [20] propose a social network short text semantic modeling method called STTM based on its spatial and temporal characteristics. However, the aggregation strategies mentioned above can be weaken the data sparsity problem in some extent, and may boost the quality of topic inference.…”

Section: A Topic Modeling For Short Textmentioning

confidence: 99%

Topic Modeling for Short Texts via Word Embedding and Document Correlation

Feng

Jiang

2020

IEEE Access

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Topic modeling is a widely studied foundational and interesting problem in the text mining domains. Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents. However, due to the limited length of short text, data sparsity impedes the inference process of conventional topic models and causes unsatisfactory results on short texts. In fact, each short text usually contains a limited number of topics, and understanding semantic content of short text needs to the relevant background knowledge. Inspired by the observed information, we propose a regularized non-negative matrix factorization topic model for short texts, named TRNMF. The proposed model leverages pre-trained distributional vector representation of words to overcome the data sparsity problem of short texts. Meanwhile, the method employs the clustering mechanism under document-totopic distributions during the topic inference by using Gibbs Sampling Dirichlet Multinomial Mixture model. TRNMF integrates successfully both word co-occurrence regularization and sentence similarity regularization into topic modeling for short texts. Through extensive experiments on constructed real-world short text corpus, experimental results show that TRNMF can achieve better results than the state-of-the-art methods in term of topic coherence measure and text classification task.

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Section: A Topic Modeling For Short Textmentioning

confidence: 99%

Topic Modeling for Short Texts via Word Embedding and Document Correlation

Feng

Jiang

2020

IEEE Access

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“…Sharma et al [25] introduced a crime intensity geographic model to detect the safest path between two locations, which employs a naive Bayes classifier with features derived from the LDA model. Kou et al [26] proposed a Spatial and Temporal Topic Model (STTM), which focuses on analyzing influential social events based on both spatial and temporal characteristics.…”

Section: A Topic Models In Applicationsmentioning

confidence: 99%

Collaboratively Modeling and Embedding of Latent Topics for Short Texts

et al. 2020

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Deriving a successful document representation is the critical challenge in many downstream tasks in NLP, especially when documents are very short. It is challenging to handle the sparsity and the noise problems confronting short texts. Some approaches employ latent topic models, based on global word co-occurrence, to obtain topic distribution as the representation. Others leverage word embeddings, which consider local conditional dependencies, to map a document as a summation vector of them. Unlike the existing works which explore the strategy of utilizing one to help the other, i.e., topic models for word embeddings or vice versa, we propose CME-DMM, a collaboratively modeling and embedding framework for capturing coherent latent topics from short texts. CME-DMM incorporates topic and word embeddings through the attention mechanism and implants them into the latent topic models, which significantly improve the quality of latent topics. Extensive experiments demonstrate that CME-DMM could perceive more coherent topics than other popular methods, resulting in a better performance in downstream NLP tasks such as classification. Besides the interpretable latent topics, the corresponding topic embeddings can describe the meanings of latent topics in the semantic space. The attention vectors, as a by-product of the learning process, can identify the keywords in noisy short texts.

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“…We use the open source Chinese word segmentation tool [39] developed by Tsinghua University for word segmentation. Meanwhiles, follow [40], we delete stop words and words appearing fewer than 6 times. The detail statistics information of processed dataset is listed in Table 2.…”

Section: A Datasetmentioning

confidence: 99%

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

2020

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With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatialtemporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method. INDEX TERMS public opinion topic, social network, topic model, topic identification.

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A semantic modeling method for social network short text based on spatial and temporal characteristics

Cited by 31 publications

References 33 publications

Topic Modeling for Short Texts via Word Embedding and Document Correlation

Topic Modeling for Short Texts via Word Embedding and Document Correlation

Collaboratively Modeling and Embedding of Latent Topics for Short Texts

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

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