# Integrating Text Classification into Topic Discovery Using Semantic Embedding Models
## 2 Related Works  
This section presents works related to the same field. Some works incorporated additional algorithms into their approaches to discover topics such as text classification through deep learning models. They also applied sentiment analysis and clustering algorithms for the same purpose. In the literature, some authors created different models for discovering topics. The authors in [14; 15; 16; 17; 18; 19] used the normalized topic coherence metric to evaluate the results obtained. In [14], a topic model based on min-hashing was proposed to find sets of matching words, which were subsequently grouped to produce the existing topics in the analyzed text. On the other hand, in [17; 20; 21; 22; 23], the authors applied different models, like encoder, LSTM, and matrix factorization in their research. In [17], the authors combined contextualized representations with topic models via neural networks. The combination presents an extension of the neural-named ProLDA model. On the other hand, ref. [18] proposed a variational automatic encoder (VAE) NTM model. The model reconstructs the sentence and word count of the document by using combinations of bag-of-words and word embedding. Furthermore, in [24], the authors showed the pseudo-document-based topic model (PTM), which introduces the concept of a pseudo-document to add short text against the scarcity of data implicitly. They also proposed a word embedding PTM (WE-PTM). On the other hand, in [21], a hierarchical latent tree analysis was proposed for hierarchical topic modeling, with the extracting and selecting collocations as a preprocessing step. The model was named HLTA. Selected collocations were replaced with unique tokens in the bag-of-words model before running HLTA. In [22], the authors presented the automated extraction of discussions related to COVID-19 and applied an LSTM recurrent neural network for sentiment classification. In [23], they showed two mixed counting neural models, called the negative binomial-neural topic model (NB-NTM) and the gamma negative binomial-neural topic model (GNB NTM). However, in [23], the authors showed two models for discovering scattered topics. The first model involved the negative binomial-neural topic model (NB-NTM) subjects, and the second involved gamma negative binomial-neural topic model (GNB-NTM) subjects. In [20], the authors presented two approaches, NTM-R and NTM-F, which were based on regularization and factorization constraints. The objective was to incorporate knowledge about topic coherence in formulating topic models. The authors of [19; 25; 26; 27; 28; 29; 30] applied word embedding to discover topics in long and short texts, or they applied clustering algorithms. Hence, in [25; 26], the authors showed models based on word embeddings. In [25], the authors presented a model named Word2 Vec2 Graph, based on the Word2 Vec model. The authors applied the model to analyze long documents, obtain unexpected word associations, and discover topics in the papers. On the other hand, in [26], the authors presented an approach to topic discovery and extracted text representations of tweets using a word embedding model. They then grouped them into semantically similar groups using the HDBSCAN algorithm, with each representing a topic. In addition, in [19], the authors presented a hierarchical topic modeling algorithm. The algorithm is based on community mining of word co-occurrence networks, taking advantage of the natural network structure. However, a Bayesian generative model was shown in [27]. The model describes thematic hierarchies organized into taxonomies. The experiments show that the proposed model integrates prior knowledge and improves both the hierarchical discovery of topics and the representation of documents. In [28], the authors presented the use of the kernel principal component analysis (KernelPCA) and \(K\)-means clustering in BERTopic architecture.