Mass Media Evaluation Using Topic Modelling

Yakunin, Kirill; Mukhamediev, Ravil I.; Mussabayev, Rustam; Buldybayev, Timur; Kuchin, Yan; Murzakhmetov, Sanzhar; Yunussov, Rassul; Ospanova, Ulzhan

doi:10.1007/978-3-030-65218-0_13

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Similarly, when topic models have been applied to aviation incident documents, unreported issues and safety conditions have been disclosed [15]. The scope of topic modelling is comprehensive, and is applicable to textual corpora across a breadth of applications [5][6][7].…”

Section: Topic Modelling Applicationsmentioning

confidence: 99%

“…The search targeted journal articles that included the term 'consumer' (or common synonyms for the term 'consumer') and the term 'behavior' or 'attitude' within their title, keywords, or abstract sections. 6 Given the concept of attitude has a significant influence over behavior [47], and hence occupies a central position for consumer behavior research [48], this was also included within the search for publications. A total of 11,841 peer-reviewed articles from 882 journals were retrieved, each published between the 1 st of January 1999 to the 31 st of March 2019.…”

Section: Data Descriptionmentioning

confidence: 99%

“…A series of structural topic models with a different number of topics (ranging from 10 to 120) were then fitted, to which the solution comprised of 70 topics was selected. Details for the evaluation of model fitness that guided the selection of the final solution are reported in 6 Search Query: (consumer OR customer OR shopper OR buyer OR purchaser OR client) AND (behavior OR behavior OR attitude). 7 The full body of text for each of the 11,841 articles were retrieved, which were used to establish the corpus.…”

Section: Topic Modellingmentioning

confidence: 99%

“…Topic modelling is an unsupervised process for identifying interpretable latent topics from a collection of documents, and has been utilized by researchers and analytical practitioners for various applications [3,4]. Examples include the evaluation of online hotel reviews to understand guest experiences [5], monitoring mass media to detect change in public opinion [6], inspecting SMS messages for spam filtering [7], examining financial disclosure statements for the detection of fraud and misreporting [8], and assessing electronic medical records to support the diagnosis of health conditions [9].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics

Camilleri

Miah

2021

J Big Data

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In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way.

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Section: Topic Modelling Applicationsmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

Section: Topic Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics

Camilleri

Miah

2021

J Big Data

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“…Topic modeling is a statistical analysis method to discover latent semantic structures hidden in texts [1]. Topic modeling in the natural language processing (NLP) field is one of the main methods used in various applications: information retrieval [2], classification [3], categorization [4], document annotation [5], classification of social significant information [6], propaganda identification [7], mass media evaluation [8], social network analysis [9], etc. The topic model identifies which terms compose each topic and which topics are related to each text.…”

Section: Introductionmentioning

confidence: 99%

A New Sentence-Based Interpretative Topic Modeling and Automatic Topic Labeling

2021

Self Cite

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This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence embeddings. The topic model estimates discrete distributions of sentence occurrences within topics and discrete distributions of topic occurrence within the text. Our approach provides the possibility of explicit interpretation of topics since sentences, unlike words, are more informative and have complete grammatical and semantic constructions inside. The method for automatic topic labeling is also provided. Contextual embeddings based on the BERT model are used to obtain corresponding sentence embeddings for their subsequent analysis. Moreover, our approach allows big data processing and shows the possibility of utilizing the combination of internal and external knowledge sources in the process of topic modeling. The internal knowledge source is represented by the text corpus itself and often it is a single knowledge source in the traditional topic modeling approaches. The external knowledge source is represented by the BERT, a machine learning model which was preliminarily trained on a huge amount of textual data and is used for generating the context-dependent sentence embeddings.

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