Topic Modelling with NMF vs. Expert Topic Annotation: The Case Study of Russian Fiction

Sherstinova, Tatiana Y.; Mitrofanova, Olga; Skrebtsova, Tatiana G.; Zamiraylova, Ekaterina; Kirina, Margarita

doi:10.1007/978-3-030-60887-3_13

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Topic modelling abstract subjects in data corpus based on word clusters alongside their corresponding frequency contained in each text document [23]. It is often deployed for natural language processing (NLP) to uncover topical subjects and thereby extract semantic meaning from unordered text documents in applications such as opinion and text mining and in information retrieval systems.…”

Section: Topic Modelling Of Preprocessed Datamentioning

confidence: 99%

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Olaleye¹

2022

IJRASET

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Data has indisputably proven overtime to have a better idea and with the surge of big data in the era of coronavirus, research initiatives in the field of data mining continues to leverage computational methodologies. Owing to the dreadful nature of the Omicron-variant, a fight or flight dilemma readily pervades college communities far reaching implications on work ethics of academic front-liners. This study therefore aim to gain insights from academia-sourced data to unravel fear-stimulus in college communities. The predictive analytics is carried out on college-based opinion poll. The Valence Aware Dictionary for Sentiment Reasoning algorithm is deployed for emotion analytics while the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are employed for topic modelling. The REPTree algorithm models the fear-spotting decision tree using 10-fold cross-validation. Experimental results shows a high performance metrics of 94.68% on Recall and Precision as the hand-washing attribute is returned as the most significant variable with highest information gain. Results of topic modelling likewise returns non-clinical precautionary measures as fear stimulus while the Vader sentiment analysis shows a 22.47%, 25.8%, and 51.73% positive, negative, and neutral polarity scores respectively, indicative of the academic front-liners’ pessimism towards effective safety measure compliance with non-clinical regulations. Keywords: COVID-19, Omicron, Sentiment Analysis, Topic Modelling.

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Section: Topic Modelling Of Preprocessed Datamentioning

confidence: 99%

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Olaleye¹

2022

IJRASET

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“…Время сдвигается в сторону «вечера» и «ночи», акцент с нейтрального «дом» в теме 16(2-100) смещается на «дверь» и «окно», которые можно считать метафорами расставания/встречи и ожидания. В теме 22(2-100) возникает мотив «письма», весьма частотный для русской прозы того периода [35], но особенно значимый во время войны и вынужденной разлуки, а также мотив мысли, наполняющий человеческое сознание в тревожные времена. Примером рассказа, относящегося и к 16, и к 22 топикам, могут служить «Ситцевые колокольчики» Ю. Л. Слезкина (1922).…”

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Topic modeling of the Russian short stories of 1900–1930s: the most frequent topics and their dynamics

Sherstinova¹,

Moskvina²,

Kirina³

et al. 2022

Computational Linguistics and Intellectual Technologies

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The article describes the results of an experiment on topic modeling of Russian short stories for three successive historical periods of the early 20th century: 1) the beginning of the 20th century until 1913, 2) the warrevolutionary period (1914–1922), and 3) the early Soviet period (1923-1930). Using the Latent Dirichlet Allocation (LDA) algorithm, 9 models were built — 3 samples of different sizes (100, 500, and 1000 stories) for each of the periods. It turned out that in every model there are very frequent “themes” (topics) that characterize with a high probability a fairly significant share of texts in each sample. Moreover, one can also observe a meaningful dynamics of these frequent topics over different time periods, which allows us to consider them as thematic and stylistic markers of the analyzed text collections along with the more traditional quantitative measures of text analysis. The variety of frequent topics turned out to be higher in the second and third periods, which can be explained by the greater lexical and stylistic diversity of the prose of the “era of change”.

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Normalization Issues in Digital Literary Studies: Spelling, Literary Themes and Biographical Description of Writers

Sherstinova

Kirina

2022

Communications in Computer and Information Science

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Topic Modelling with NMF vs. Expert Topic Annotation: The Case Study of Russian Fiction

Cited by 8 publications

References 16 publications

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Topic modeling of the Russian short stories of 1900–1930s: the most frequent topics and their dynamics

Normalization Issues in Digital Literary Studies: Spelling, Literary Themes and Biographical Description of Writers

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