Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies

Silva, Nádia Félix Felipe da; Silva, Marília Costa R.; Pereira, Fabíola S. F.; Tarrega, João P.; Beinotti, João Vitor P.; Fonseca, Márcio Alves da; Andrade, Francisco Edmundo de; Carvalho, André C. de L. F. de P.

doi:10.1007/978-3-030-91699-2_8

Cited by 14 publications

(6 citation statements)

References 34 publications

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“…Evaluating the effectiveness of LDA, LSA, PLSA, NMF, and BERTopic in extracting topics from Afaan Oromo, Amharic, and Tigrigna social media data is crucial for assessing semantic coherence and topic diversity. Topic coherence is a linguistic concept that automatically evaluates the interpretability of latent topics based on the distributional theory, which suggests similar meanings often appear in similar contexts [16]. The study confirms that the coherence measurement CV is a reliable tool for evaluating the effectiveness of topic modeling, showing a positive correlation with human interpretability [21].…”

Section: F Evaluationsupporting

confidence: 65%

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Adapting Outperformer from Topic Modeling Methods for Topic Extraction and Analysis: The Case of Afaan Oromo, Amharic, and Tigrigna Facebook Text Comments

Defersha,

Tune,

Abate

2024

IJACSA

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Facebook users generate a vast amount of data, including posts, comments, and replies, in various formats such as short text, long text, structured, unstructured, and semi structured. Consequently, obtaining import information from social media data becomes a significant challenge for low-resource languages such as Afaan Oromo, Amharic, and Tigrigna. Topic modeling algorithms are designed to identify and categorize topics within a set of documents based on their semantic similarity which helps obtain insight from documents. This study proposes latent Dirichlet allocation, matrix factorization, probabilistic latent semantic analysis, and BERTopic to extract topics from Facebook text comments in Afaan Oromo, Amharic, and Tigrigna. The study utilized text comments from the Facebook pages of various individuals, including activists, politicians, athletes, media companies, and government offices. BERTopic was found to be the most effective for identifying major topics and providing valuable insights into user conversations and social media trends with coherence scores of 82.74%, 87.85%, and 81.79% respectively.

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Section: F Evaluationsupporting

confidence: 65%

“…Silva et al [16] employ BERTopic topic models for Portuguese political comments on Brazil's Chamber of Deputies bills, adjusting parameters to improve accuracy and align with research direction.…”

Section: Introductionmentioning

confidence: 99%

Adapting Outperformer from Topic Modeling Methods for Topic Extraction and Analysis: The Case of Afaan Oromo, Amharic, and Tigrigna Facebook Text Comments

Defersha,

Tune,

Abate

2024

IJACSA

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“…Bilbao-Jayo and Almeida (2018) proposed a multi-scale convolutional neural networks model on classifying party manifestos that could be potentially used to automate the analysis of the political discourse to allow public administrations to better react to their needs and claims. Da Silva et al (2021) used deep learning based embedding in topic modeling to online comments regarding two legislative proposals in Brazil's Chamber of Deputies and demonstrated the applicability of the topic modeling tools for discovering latent topics that could be potentially used to increase citizen engagement regarding governmental policies.…”

Section: Selecting and Formulating Statements For The Vaa Questionnairementioning

confidence: 99%

Artificial intelligence and voting advice applications

Gemenis

2024

Front. Polit. Sci.

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The voter information tools collectively known as “Voting Advice Applications” (VAAs) have emerged as particularly popular tools in the realm of E-participation. Today, VAAs are integral parts of election campaigns in many countries around the world as they routinely engage millions of citizens, in addition to political actors and the media. This contribution assesses the integration of Artificial Intelligence (AI) in the design and dissemination of VAAs, considering normative, ethical, and methodological challenges. The study provides a comprehensive overview of AI applications in VAA development, from formulating questions to disseminating information, and concludes by highlighting areas where AI can serve as a valuable tool for enhancing the positive impact of VAAs on democratic processes.

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“…BERTopic, proposed by Grootendorst [25], has progressively gained popularity as a tool for dynamic topic modeling [15], [26], [27]. Figure 1 shows the main components of the BERTopic algorithm.…”

Section: Implementation Of Dynamic Topic Modeling To Discover Topic E...mentioning

confidence: 99%

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Hananto

2023

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Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.

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Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies

Cited by 14 publications

References 34 publications

Adapting Outperformer from Topic Modeling Methods for Topic Extraction and Analysis: The Case of Afaan Oromo, Amharic, and Tigrigna Facebook Text Comments

Adapting Outperformer from Topic Modeling Methods for Topic Extraction and Analysis: The Case of Afaan Oromo, Amharic, and Tigrigna Facebook Text Comments

Artificial intelligence and voting advice applications

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

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