## 2 Related Work  
### Relevant Studies  
In the context of text classification, topic modeling can serve as both a feature projection (FP) and a feature selection (FS) technique. Extensive studies have used TM as a feature projection method [14; 15; 16; 17]; however, in this study, we focus on its application as a feature selection method, where the topic-word matrix output is utilized to identify a subset of relevant terms to train the text classifier.  
[PERSON] et al. [18] conducted a study that utilized LDA with Gibbs sampling as an FS method for text classification. The authors leveraged the topic-term matrix to identify words with lower entropy to train a state-of-the-art classifier. Instead of using Gibbs sampling for estimation, [PERSON] et al. [19] conducted a similar study using variational expectation maximization.  
In contrast to using LDA, [PERSON] et al. [20] employed labeled LDA (LLDA), a supervised variant of LDA, as an FS method to enhance the weak learning of Ada-Boost. LLDA takes into account the categorical structure of documents to generate topics corresponding to the number of categories. Each word is assigned a probability for each topic. According to their probabilities, a specific number or percentage of training features are selected and subsequently utilized to train the classification model.  
Instead of selecting individual words, [PERSON] and [PERSON] [9] introduced TextNetTopics, an LDA-based topic selection approach for text classification. After scoring LDA topics using a cross-validated accuracy topic-scoring mechanism, TextNetTopics selects the top \(r\)-ranked topics to train the text classifier, where each topic represents a set of semantically related terms. The aim of this approach is to maintain the benefits of feature interaction offered by topics while eliminating topics that could introduce noise and potentially degrade the model's performance.  
Building on TextNetTopics, [PERSON] et al. [10] introduced TextNetTopics Pro, an innovative method designed specifically for short text classification. This approach combines topics of words and topic distribution extracted by a short text topic model (e.g., GSDMM,BTM), aiming to overcome the challenge of data sparsity in short text classification. The foundation of TextNetTopics and TextNetTopics Pro is the G-S-M approach [11].  
However, TextNetTopics has three main shortcomings that limit its effectiveness in text classification tasks: First, TextNetTopics selects topics extracted by LDA with all the features they contain to train a machine learning model. This method may include redundant or irrelevant features, which can negatively impact classification performance in addition to increasing the size of the feature subset. Second, TextNetTopics employs a topic-scoring mechanism that becomes more computationally intensive as the number of topics increases due to the independent training of each topic and the use of cross-validated accuracy as the scoring metric. Finally, LDA does not explicitly model semantic relationships between words: it captures semantic information indirectly through statistical co-occurrence patterns.  
This study aims to overcome these limitations by proposing faSTNT, an advancement of TextNetTopics which incorporates a filtering component to remove the least informative features using random forest feature importance values, employs a more efficient scoring mechanism that considers topic interactions, and leverages pre-trained word embeddings to model semantic relationships between words explicitly.  
The following subsections introduce essential concepts relevant to our study.