Experimental explorations on short text topic mining between LDA and NMF based Schemes

Chen, Yong; Zhang, Hui; Li, Rui; Z, Ye; Lin, Jianying

doi:10.1016/j.knosys.2018.08.011

Cited by 174 publications

(128 citation statements)

References 23 publications

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“…It can be used to automatically extract valuable hidden elements. Conventional topic modeling techniques such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) are widely used to infer latent topical structures from documents [9], [14], [20], [23].…”

Section: B Topic Modeling Based On Contentsmentioning

confidence: 99%

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

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Section: B Topic Modeling Based On Contentsmentioning

confidence: 99%

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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“…Vector representation is transforming meaningful words with context terms represented as number, where each word is distributed with weights [26]. Various forms of extracting words are: Frequency based Embedding [27], Prediction based Embedding [28], Non-Negative Matrix Factorization [29]. TF-IDF (Term-Frequency -Inverse document frequency) [30], Count Vector, Co-occurrence vector and document frequency.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Google News - [22], [66], [80] Snippets 8 [8], [29], [36], [44], [45], [49][50][51][52], [68], [74], [75] NIPS - [38] DBLP 6 [31], [37], [62] Yahoo! Answers 11 [38], [45], [77], [82] Online News 7 [26][27][28][29], [31], [32], [37], [47], [62], [68], [81] Baidu Q & A 35 [49], [52], [37], [81][35] [15], [39], [75] Satck Overflow Q & A - [29], [44], [47] Tweets {August to Oct 2008}…”

Section: Short Textmentioning

confidence: 99%

A Detailed Survey on Topic Modeling for Document and Short Text Data

Likhitha¹,

Junior²,

H.³

2019

IJCA

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“…As we shall see, we propose a new topic extraction strategy to mitigate these problems. In [32] the authors proposed a novel model called ''knowledge-guided nonnegative matrix factorization for better short text topic mining'' (abbreviated as KGNMF), KGNMF integrated the word-word semantic graph regularization which can be learned from external knowledge base e.g., Wikipedia into the basic NMF for improvement in short text topic learning. In [33] the authors developed a general non-probabilistic topic modeling framework.…”

Section: Latent Topic Decompositionmentioning

confidence: 99%

“…Due to the semantic text representation approach specially WSD in addition the semantic similarity, the conventional topic models are not effectively to capture semantic topics, which leads to the poor performance in [33] X √ Schneider et al [22] X X Zhao et al [23] √ X Chen et al [32] X √ Hong et al [34] X X Peng et al [47] X X Xu et al [26] X X Li et al [24] √ X Allahyari et al [25] √ X Izquierdo et al [48] √ X Viegas et al [35] √ (word embedding) √ semantic topic learning. To tackle this problem, we introduce semantic topic modelling SNNMF.…”

Section: Problem Statementmentioning

confidence: 99%

Novel semantic tagging detection algorithms based non-negative matrix factorization

2019

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The tagging aims to address a challenge to search relevant text-documents given a set of tags. In addition, the tag-based approaches received a wide attention as a possible solution to the big-content. Probabilistic topic model methods, such as Dirichlet distribution and non-negative matrix factorization are used for tagging process. Both have many challenges. The iterations in addition the semantic coherence are considered as challenges in semantic tagging applications. In light of this, we propose a novel learning tagging model called semantic non-negative matrix factorization, which introduces the utilization of the semantic text representation via knowledge-based approach to extract the term-topic matrix and the topic-document matrix by semantically approach. The proposed words are based on a novel initialization method for non-negative matrix factorization technique. In the experimental evaluations, we use five datasets demonstrate the effectiveness of our model. The results are compared with the state-of-the-art model. The results show the proposed model has an ability to generate more precise topics with semantically related and having the high sense to the disambiguation of meaning, provides up to more dimensionality reduction and the topic coherence based semantic.

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Experimental explorations on short text topic mining between LDA and NMF based Schemes

Cited by 174 publications

References 23 publications

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

A Detailed Survey on Topic Modeling for Document and Short Text Data

Novel semantic tagging detection algorithms based non-negative matrix factorization

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