SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling

Shahriar, Khandaker Tayef; Moni, Mohammad Ali; Hoque, Mohammed Moshiul; Islam, Muhammad Nazrul; Sarker, Iqbal H.

doi:10.20944/preprints202204.0026.v1

Cited by 2 publications

(2 citation statements)

References 17 publications

(18 reference statements)

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“…Authors in [20] extracted a summary of documents belonging to a topic as a candidate label and the most similar candidate label vector to the topic vector as a topic label. The authors in [24] used sentiment-based and aspect-based cluster terms to label the tweets related to the COVID-19 pandemic. The contextual information, however, is missed by term-based strategies.…”

Section: A Stat-based Approchesmentioning

confidence: 99%

Advanced Hierarchical Topic Labeling for Short Text

et al. 2023

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Hierarchical Topic Modeling is the probabilistic approach for discovering latent topics distributed hierarchically among the documents. The distributed topics are represented with the respective topic terms. An unambiguous conclusion from the topic term distribution is a challenge for readers. The hierarchical topic labeling eases the challenge by facilitating an individual, appropriate label for each topic at every level. In this work, we propose a BERT-embedding inspired methodology for labeling hierarchical topics in short text corpora. The short texts have gained significant popularity on multiple platforms in diverse domains. The limited information available in the short text makes it difficult to deal with. In our work, we have used three diverse short text datasets that include both structured and unstructured instances. Such diversity ensures the broad application scope of this work. Considering the relevancy factor of the labels, the proposed methodology has been compared against both automatic and human annotators. Our proposed methodology outperformed the benchmark with an average score of 0.4185, 49.50, and 49.16 for cosine similarity, exact match, and partial match, respectively.

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Section: A Stat-based Approchesmentioning

confidence: 99%

Advanced Hierarchical Topic Labeling for Short Text

et al. 2023

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“…One study proposed a framework that dynamically identifies key topics with labels from COVID-19 tweets using Latent Dirichlet Allocation (LDA) generated topics [18]. Another study examined linguistic and visual communication in UK public health agency tweets related to COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Uncovering COVID-19 conversations: Twitter insights and trends

Selim Molla,

Ehsan Bazgir,

S M Mustaquim

et al. 2024

World J. Adv. Res. Rev.

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In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.

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SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling

Cited by 2 publications

References 17 publications

Advanced Hierarchical Topic Labeling for Short Text

Advanced Hierarchical Topic Labeling for Short Text

Uncovering COVID-19 conversations: Twitter insights and trends

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