## 2 Literature Review  
Currently, several modern applications, such as e-learning platforms, social networks, or digital libraries, are able to collect more and more textual data [1]. However, the exploitation of this data is rather limited. In particular, there are few approaches that are able to perform the analysis automatically and without user involvement. Text mining has been adopted in various sectors over the years, as illustrated in [9]. It is based on algorithms capable of deriving high-quality information from a large collection of documents. Its activities include: (i) grouping documents with similar properties or similar content [1, 10, 11], (ii) topic modeling [3, 12, 13, 14, 15, 16, 17, 18] and detection [19, 20, 21], (iii) classification models [22, 23, 24], (iv) opinion mining and sentiment analysis [25, 26], and (vi) document querying [27]. Computational cost is a non-negligible issue when applying the above techniques to a large data collection. To address this issue, there have been several research efforts focused on developing innovative algorithms and methods to support large-scale analytics based on MapReduce [28]. Another improvement has been achieved with Apache Spark [29], which surpassed Hadoop's performance due to its distributed memory abstraction, a primary aspect for data analytics algorithms. In the scientific research, several approaches and solutions have been presented in order to represent, mine, and retrieve information [30] from text sources. Depending on the modeling of the text data and the used techniques, different models have been proposed in the scientific literature: set-theoretic [31] (such as the Boolean models, representing documents as sets of words or phrases), algebraic [1, 32, 33] (representing documents as vectors or matrices, such as the vector space models, the latent semantic analysis, the principal components analysis (PCA) [34], or the sparse latent analysis [35]), and probabilistic [36, 37] (such as the latent Dirichlet allocation, which represents documents as probabilities of words, or the probabilistic latent semantic analysis). Figure 1 provides an overview of the state of the art in topic modeling and recognition methods. Based on the proposed methodology, the studies can be divided into unsupervised and (semi-) supervised approaches. The work proposed in [16, 17, 20, 24, 38] belongs to the (semi-) supervised methods. In [20], the authors propose a framework to improve topic detection based on text and image information. After applying image understanding through deep learning techniques, they integrate the results with short textual information. Instead, ref. [24] shows a semi-supervised approach. They present two frameworks: the first models short texts, while the second embeds the first for short-text classification. In [16], the authors address topic detection on tweets related to COVID-19 in English and Portuguese. Additionally, in [38], the authors use COVID-19 tweets as data, but they rely on a naive Bayes classifier and logistic regression. In [17], the authors combine a heterogeneous attention network with a DBSCAN algorithm and a pairwise popularity graph convolutional network in order to detect social streaming events and study how they evolve in time. Another research trend that has emerged in recent years is the integration of word embedding and clustering techniques, as seen in [14, 15]. The main idea is to extract word embeddings from models such as BERT and to apply clustering techniques to them. A variant of this strategy is proposed in [18]. Here, the authors modify the creation of the word embedding by constraints, and then apply a deep K-means algorithm. In [13], they combine traditional topic models, such as LDA, with word embeddings. Other authors instead rely on more traditional approaches and focus their research efforts on other aspects.