Leveraging unstructured information using topic modelling

Uys, J.W.; Preez, N.D. du; Uys, E.W.

doi:10.1109/picmet.2008.4599703

Cited by 31 publications

(9 citation statements)

References 14 publications

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“…The study in [9] used In-house developed software for implementing the LDA model for analysis tiny document group (62 documents) of health-related issues to get an overview of the kinds of health information that are labeled by the documents in the related corpus, and also to get a list of documents concerning to the scope of mental health [10] utilized topic modeling (LDA) and SVM methods in clinical reports for analyzing the classification of CT imaging reports into binary classes which show the system ability for effectively and interpretable representation of them also the model was appropriate in reducing the dimensional. This study showed improvement for datasets with equal class distribution over baseline approaches.…”

Section: Methodsmentioning

confidence: 99%

LSA & LDA topic modeling classification: comparison study on e-books

Mohammed

Al-augby

2020

IJEECS

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With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was (0.592179) of coherence value when the number of topics was 20 while the LSA coherence value was (0.5773026) when the number of topics was 10.

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Section: Methodsmentioning

confidence: 99%

LSA & LDA topic modeling classification: comparison study on e-books

Mohammed

Al-augby

2020

IJEECS

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“…Typically, clustering algorithms select the number of clusters by representing the sum of squared error versus the number of clusters and then applying the elbow criterion. However, when dealing with a corpus of documents, it is also important to apply the analyst judgement (Uys et al, 2008). Some level of granularity is desired but avoiding either superfluous detail or a too coarsely grained solution.…”

Section: Content Analysismentioning

confidence: 99%

Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

Teso

Olmedilla

Torres

et al. 2018

Technological Forecasting and Social Change

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Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective http://researchonline.ljmu.ac.uk/id/eprint/8002/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. Teso, E, Olmedilla, M, Martínez Torres, R and Toral, S (2018) Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technological Forecasting and Social Change. Abstract:The emergence of online user-generated content has raised numerous questions about discourse gender differences as compared to face-to-face interactions. The intended gender-free equality of Internet has been challenged by numerous studies, and significant differences have been found in online communications. This paper proposes the application of text mining techniques to online gender discourse through the analysis of shared reviews in electronic word-of-mouth communities (eWOM), which is a form of user-generated content. More specifically, linguistic issues, sentiment analysis and 2

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“…Topic models generate interpretable, semantically coherent issues, which can examine by enumerating the most likely words for each subject [7]. The output of topic modeling is a set of topics consisting of clusters of words that co-occur in these documents according to specific patterns Topic models are useful for a variety of tasks such as organization, classification, collaborative filtering and information retrieval [8]. There are several approaches to implement topic modeling.…”

Section: Topic Modellingmentioning

confidence: 99%