Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model

Chen, Wei; Yu, Zhengtao; Xian, Yantuan; Wang, Zhenhan; Wen, Yonghua

doi:10.4018/ijisss.2020040106

Cited by 4 publications

(1 citation statement)

References 14 publications

(1 reference statement)

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“…Chen proposed an unsupervised keyword mining method based on graph ranking, which is more effective than other methods of collecting CNKI literature abstracts and news corpora. They predicted that integrating domain knowledge into the model to improve the mining effect is a development direction [55]. However, the number of experimental samples is limited, and there is no further study on the potential needs of users.…”

Section: Application Of Data Miningmentioning

confidence: 99%

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

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In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

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Section: Application Of Data Miningmentioning

confidence: 99%

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

Information

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Library Similar Literature Screening System Research Based on LDA Topic Model

Gao,

Cui,

Zhang

2024

J. Info. Know. Mgmt.

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Science and technology are highly inheritable undertakings, and any scientific and technological worker can make good progress without the experience and achievements of predecessors or others. In the face of an ever-expanding pool of literature, the ability to efficiently and accurately search for similar works is a major challenge in current research. This paper uses Latent Dirichlet Allocation (LDA) topic model to construct feature vectors for the title and abstract, and the bag-of-words model to construct feature vectors for publication type. The similarity between the feature vectors is measured by calculating the cosine values. The experiment demonstrated that the precision, recall and WSS95 scores of the algorithm proposed in the study were 90.55%, 98.74% and 52.45% under the literature title element, and 91.78%, 99.58% and 62.47% under the literature abstract element, respectively. Under the literature publication type element, the precision, recall and WSS95 scores of the proposed algorithm were 90.77%, 98.05% and 40.14%, respectively. Under the combination of literature title, abstract and publication type elements, the WSS95 score of the proposed algorithm was 79.03%. In summary, the study proposes a robust performance of the literature screening (LS) algorithm based on the LDA topic model, and a similar LS system designed on this basis can effectively improve the efficiency of LS.

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Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary

Zaw

Tandayya

2021

International Journal of Information Systems in the Service Sector

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The customer feedbacks provide alternative and important sources to discover knowledge supporting the marketers and customers to make better decisions. However, the manual process to extract useful information depends on domain experts. This paper focuses on improving the performance of the automatic sentiment information extraction from customer feedbacks. The article proposes a new extraction method that consider multiple dimensions of feedback information, aspect, word, contrast, sentence or phrase, and document levels. The aspect-based sentiment extraction uses a named entity recognition technique to extract the desired aspects of a target product. The aspect-based sentiment combines with sentiment information from multiple levels of feedback contexts resulting in the fused sentiment information improves the extraction performance. We validate the effectiveness by measuring the accuracy of the sentiment and aspect recognition methods comparing with SentiStrength and Word-Count. This information gives some insights on customer satisfaction and can be applied in an alarming tool.

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Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model

Cited by 4 publications

References 14 publications

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Library Similar Literature Screening System Research Based on LDA Topic Model

Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary

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