Text mining based theme logic structure identification: application in library journals

Zhu, Qing; Wu, Yiqiong; Li, Yuze; Han, Jing; Zhou, Xiaoyang

doi:10.1108/lht-10-2017-0211

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…Zhu et al took the positive and negative words in GI (General Inquirer) and WordNet as seed words, obtained an expanded large-scale emotional word set, and used this as a classification feature, using the machine. e learning method automatically classifies the positive and negative meanings of texts [11]. Short uses "good" and "bad" as the seed words of sentiment words and uses WordNet to calculate the semantic distance between the new word and the two words, and this semantic distance is used to determine the tendency of words [12].…”

Section: Related Workmentioning

confidence: 99%

The Identification and Dissemination of Creative Elements of New Media Original Film and Television Works Based on Review Text Mining and Machine Learning

2022

Mathematical Problems in Engineering

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With the continuous growth of the film market and the consumption demand of audiences, the value of film content has become increasingly apparent. Extracting movie content elements is an important step in quantitative analysis of movie content. In this paper, based on text mining technology, deep data analysis of movie reviews is carried out using TF-IDF and machine learning to visualize high-frequency words, and sentiment analysis is performed on reviews to find out the hidden deep information behind the big data of movie reviews. Although the extracted keywords can draw the content and characteristics of the film for us, they cannot establish the correlation with the creative elements of the film and television works. Therefore, in this paper, the extracted keywords are clustered to find the central words of these words. First of all, in order to improve the representativeness of keywords, the Epoch data set and the Batchsize data set are used to conduct experimental analysis on the model in this paper. Through comparative experiments, it is concluded that when Batchsize = 32 and Epoch = 25, the model achieves the optimal classification effect. The analysis shows that when the training times are too small, the model is not fully learned, and when the training times are too many, the model will be overfitted, which will reduce the accuracy of the model, resulting in the opposite effect. The keywords extracted above can draw the content and characteristics of the movie for us.

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Section: Related Workmentioning

confidence: 99%

The Identification and Dissemination of Creative Elements of New Media Original Film and Television Works Based on Review Text Mining and Machine Learning

2022

Mathematical Problems in Engineering

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“…Topic analysis from literature, such as topic clustering and topic extraction, is of great interest to government policymakers (J€ arvelin and Vakkari, 2021) or the research community (Arshad et al, 2019;Zhu et al, 2018). Obviously, according to topic analysis, the government can discover which topics are rising or falling, and thus they can make decisions regarding grant allocation to promising research areas.…”

Section: Topic Analysismentioning

confidence: 99%

“…Topic analysis from literature, such as topic clustering and topic extraction, is of great interest to government policymakers (Järvelin and Vakkari, 2021) or the research community (Arshad et al. , 2019; Zhu et al. , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

Cao

Cheng

Liao

2022

LHT

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PurposeHow to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.Design/methodology/approachThe authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.FindingsThe authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.Originality/valueFirst, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

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“…Text mining has gained great commercial applicability. Different search engines on the web like Google also retrieve the document based on user-entered keywords, but they do not produce any new knowledge (Zhu et al , 2018; Ngai and Lee, 2016). Beyond the knowledge generation, text mining techniques find out some relationship between less similar documents and present the data in a visual format.…”

Section: Literature Surveymentioning

confidence: 99%

Semantic key phrase-based model for document management

Bafna

Pramod

Shrwaikar

et al. 2019

BIJ

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Purpose Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer relationship management and so on. The purpose of this paper is to improve important components of document management that is keyword extraction and document clustering. It is achieved through knowledge extraction by updating the phrase document matrix. The objective is to manage documents by extending the phrase document matrix and achieve refined clusters. The study achieves consistency in cluster quality in spite of the increasing size of data set. Domain independence of the proposed method is tested and compared with other methods. Design/methodology/approach In this paper, a synset-based phrase document matrix construction method is proposed where semantically similar phrases are grouped to reduce the dimension curse. When a large collection of documents is to be processed, it includes some documents that are very much related to the topic of interest known as model documents and also the documents that deviate from the topic of interest. These non-relevant documents may affect the cluster quality. The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as term frequency-inverse document frequency matrix or as phrase document matrix. Once in structured form, a range of mining algorithms from classification to clustering can be applied. Findings In the enhanced approach, the model documents are used to extract key phrases with synset groups, whereas the other documents participate in the construction of the feature matrix. It gives a better feature vector representation and improved cluster quality. Research limitations/implications Various applications that require managing of unstructured documents can use this approach by specifically incorporating the domain knowledge with a thesaurus. Practical implications Experiment pertaining to the academic domain is presented that categorizes research papers according to the context and topic, and this will help academicians to organize and build knowledge in a better way. The grouping and feature extraction for resume data can facilitate the candidate selection process. Social implications Applications like knowledge management, clustering of search engine results, different recommender systems like hotel recommender, task recommender, and so on, will benefit from this study. Hence, the study contributes to improving document management in business domains or areas of interest of its users from various strata’s of society. Originality/value The study proposed an improvement to document management approach that can be applied in various domains. The efficacy of the proposed approach and its enhancement is validated on three different data sets of well-articulated documents from data sets such as biography, resume and research papers. These results can be used for benchmarking further work carried out in these areas.

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Text mining based theme logic structure identification: application in library journals

Cited by 9 publications

References 39 publications

The Identification and Dissemination of Creative Elements of New Media Original Film and Television Works Based on Review Text Mining and Machine Learning

The Identification and Dissemination of Creative Elements of New Media Original Film and Television Works Based on Review Text Mining and Machine Learning

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

Semantic key phrase-based model for document management

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