“…Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. …”
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“…We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. …”
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“…Therefore, this study aims to analyze public sentiment by utilizing Twitter data. Latent Dirichlet Allocation (LDA) was also conducted in this study to classify public opinion. …”
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“…We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software “Text Mind”, a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. …”
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“…For topic modeling, the text mining technique of Latent Dirichlet allocation (LDA) was applied and revealed insights on computationally processed topics. …”
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“…This study aimed to study the effectiveness of China's information disclosure by examining themes, interconnection, and timeliness of information as posted on the Weibo microblogging platform between January and April 2020. The Latent Dirichlet Allocation (LDA) topic model analysis for social networks revealed six main characteristics including a shift from 'scattered' to 'focused' communication. …”
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“…This study compares public discussion topics about gene editing on Twitter and Weibo, as wel asthe evolution of these topics over four months. Latent Dirichlet allocation (LDA) was used to generate topics for 11,244 Weibo posts and 57,525 tweets from September 25, 2018, to January 25, 2019. …”
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“…In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. …”
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“…After data cleaning and lemmatisation, a word cloud was constructed, and the most frequent words and most frequent body regions were counted. Latent Dirichlet allocation was used to perform topic modelling. …”
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“…This study used the Latent Dirichlet Allocation (LDA) combined with topic intensity to discover hot topics and development trends in the study area of CDW. …”
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“…Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a ‘word-topic’ coupling network is built. …”
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“…We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. …”
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“…Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to gain insight into each of the classes.…”
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“…Using 73,397,870 text data scraped and refined from publicly available Twitter data, this study applied Latent Dirichlet Allocation (LDA) and the dynamic topic model (DTM) to ascertain the hidden structure of the ESG-related document collection and the topics being discussed. …”
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“…We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. …”
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“…We achieve this, first, by introducing a novel landmark‐adapted basis using an intrinsic Dirichlet‐Steklov eigenproblem. Second, we establish the functional decomposition of conformal maps expressed in this basis. …”
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“…The data were collected from the Twitter accounts of Le Pen and Macron and analysed via the latent Dirichlet allocation generative statistical model. …”
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“…From the perspective of news topic modeling, this paper investigated how the Communist Youth League of China (CYLC) uses organizational information communication to serve organizational goals—“Keep the Party Assured and the Youth Satisfied” (“让党放心, 让青年满意”). Using the Latent Dirichlet allocation (LDA) algorithm, we performed a topic analysis on 1898 news articles published on the CYLC website. …”
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“…In this paper, we collected microblog data containing “Shanghai” from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. …”
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