Automatic assignment of labels in Topic Modelling for Russian Corpora

Mirzagitova, Aliya; Mitrofanova, Olga

doi:10.36505/exling-2016/07/0025/000284

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Our recent investigations are aimed to fill in the gap. In this study we used an ensemble of two graph-based methods using outer sources for candidate labels generation [24]: a) candidate labels extraction from Yandex search engine with their further ranking by TextRank (this is a graph-based model that takes into account the value of the each graph's vertex depending on how many links it forms) [25,26]; b) candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA) model [27,28]. These procedures comprise two stages: candidate extraction and ranking.…”

Section: Our Approach To Topic Labellingmentioning

confidence: 99%

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E-hypertext Media Topic Model with Automatic Label Assignment

Mitrofanova

Kriukova

Shulginov

et al. 2021

Communications in Computer and Information Science

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This article deals with the principles of automatic label assignment for e-hypertext markup. We've identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label's triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms' results: at the first stage, two experts assessed the triplet's relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0»-a label doesn't match a topic; «1»-a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.

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Section: Our Approach To Topic Labellingmentioning

confidence: 99%

“…The algorithm for candidate labels extraction from Yandex search engine (Labels-Yandex) [25,26] is an elaboration of the procedure originally designed for visual information processing [13]. At the stage of candidate extraction the first 10 topical words for each topic form a separate query to Yandex.…”

Section: Topic Labelling Using Yandexmentioning

confidence: 99%

E-hypertext Media Topic Model with Automatic Label Assignment

Mitrofanova

Kriukova

Shulginov

et al. 2021

Communications in Computer and Information Science

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“…Topic modeling belongs to natural language processing, and it identifies patterns in a text [48]. We employed latent Dirichlet allocation (LDA) in this study, which is a commonly used topic modeling approach.…”

Section: Topic Modelingmentioning

confidence: 99%

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

Xiong

Hswen

Naslund

2020

IJERPH

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Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts (called tweets) were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures.

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“…Despite the evident necessity of integration of the two logics, it is rarely found also for other inflective languages; we see this logic explicitly employed by only one group working in Slovenian (see, e.g., Maucěc et al 2004, and later works). Beside this, several works by computer linguists have suggested decisions for the Russian language, including adding automated labeling to Russian-language topics (Mirzagitova and Mitrofanova 2016) and showing the possibility of domain term extraction by topic modeling (Bolshakova et al 2013). Automatic topic labeling by a single word or phrase is expected to ease topic interpretation; working upon it continued in the recent years by comparing quality of two labeling algorithms, namely the vector-based Explicit Semantic Analysis (ESA) and graph-based method, with the former one preferred by the authors (Kriukova et al 2018).…”

Section: Computer-linguistic Approaches To Topic Modelingmentioning

confidence: 99%

Topic Modeling in Russia: Current Approaches and Issues in Methodology

Bodrunova

2020

The Palgrave Handbook of Digital Russia Studies

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Topic modeling as an instrument of probabilistic clustering for text collections has gained particular attention within the computational social science in Russia. This chapter looks at how topic modeling techniques have been developed and employed by the Russian scholars, both for Russian and other languages. We divide the works on topic modeling into methodological, applied, relational, and those dedicated to modeling quality assessment. While the methodological studies are the most developed, the works explaining the substance of the Russian-language discussions cover an important niche in political and social science. However, there is a gap between method-oriented works that treat Russian as “language as such” and the works by computational linguists who focus on Russian but treat topic modeling as a method of secondary importance.

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Automatic assignment of labels in Topic Modelling for Russian Corpora

Cited by 5 publications

References 3 publications

E-hypertext Media Topic Model with Automatic Label Assignment

E-hypertext Media Topic Model with Automatic Label Assignment

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

Topic Modeling in Russia: Current Approaches and Issues in Methodology

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