Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …

Topic modeling is a well-established technique for exploring text corpora. Conventional
topic models (eg, LDA) represent topics as bags of words that often require" reading the tea …

The recent explosion in work on neural topic modeling has been criticized for optimizing
automated topic evaluation metrics at the expense of actual meaningful topic identification …

Extracting and identifying latent topics in large text corpora have gained increasing
importance in Natural Language Processing (NLP). Most models, whether probabilistic …

Automated coherence metrics constitute an efficient and popular way to evaluate topic
models. Previous works present a mixed picture of their presumed correlation with human …

As conventional topic models rely on word co-occurrence to infer latent topics, topic
modeling for short texts has been a long-standing challenge. Large Language Models …

In this paper, we conduct sentiment analysis on social media data to study mass opinion
about offshore wind energy. We adapt three machine learning models, namely, TextBlob …

With the vast amount of social media posts available online, topic modeling and sentiment
analysis have become central methods to better understand and analyze online behavior …

Topic modeling is frequently being used for analysing large text corpora such as news
articles or social media data. BERTopic, consisting of sentence embedding, dimension …

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it
suffers from instability due to its reliance on random initialization. This results in different …