# Classification of Negative Information on Socially Significant Topics in Mass Media +
## 2 Related Works, Tools and Methods
### Topic Modeling and MCDM  
Given the variety of digital sources of information and significant volume of news, there is a growing demand for their automated analysis. The evaluation of the properties of media texts can be based on natural language processing methods, for which significant progress has been achieved. In turn, the aggregation of the values of heterogeneous properties is often performed using systems of multiple-criteria decision making (MCDM). Topic analysis or topic modeling is one of the actively applied natural language processing (NLP) methods. Topic modeling is a method based on the statistical characteristics of collections of documents, and is used for text summarization, information extraction, information retrieval, and classification [23]. The essence of this approach lies in the intuitive understanding that the documents in the collection form groups in which the frequency of words or word combinations differs. Topic modeling is used as an attempt to overcome the limitations of BERT [24] and other deep learning NLP models, such as the necessity for large volumes of manual labeling and significant computational complexity, while also preserving the idea of transfer learning by transferring knowledge from large unlabeled datasets to obtain knowledge on high-level hidden latent structures of the corpora. Topic modeling is well developed in terms of algorithms and methods based on a statistical language model, and the use of document clusters related to a set of topics allows solving problems of synonymy and polysemy of terms [25]. Probabilistic topic models describe documents (M) by discrete distribution of the set of topics (T), and topics by discrete distribution of the set of terms [26]. That is, the topic model determines the topic to which each document belongs and which words form each topic. Probabilistic latent semantic analysis (PLSA), a popular latent Dirichlet distribution (LDA)method [27], and its extension BigARTM [28], are used to build a topic model of document corpora. A detailed description of the methods is presented in Appendix A. In cases in which it is necessary to make a decision based on a variety of heterogeneous parameters and alternatives, several methods are used to take into account the relative importance of the parameters and aggregate their values as one or a small number of estimates. By their nature, such tasks are related to the field of multicriteria decision making widely used in decision support systems (DSS) [29; 30; 31], which is also applicable in NLP [32]. MCDM uses many methods to make a decision based on heterogeneous criteria, including [33]: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA) [34]; potentially all pairwise rankings of all possible alternatives (PAPRIKA) [35]; ELECTRE [36]; preference ranking technique for order performance by similarity to ideal solution (TOPSIS) [37]; Multi-attribute utility theory (MAUT) [38]; Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) [39]; VIKOR [40]; analytical hierarchy process (AHP) [41]; fuzzy logic [42]; and Bayesian networks [43; 44]. Following the Bayes equation, the conditional probability of the validity of hypothesis \(h\) conditional to the event \(e\) is expressed in the form:  
\[p(h\epsilon)=\frac{p(e|h)\times p(h)}{p(e|h)\times p(h)+p(e|\sim h)\times p( \sim h)} \tag{1}\]  
where \(p(e|h)\) is the conditional probability of occurrence of the event \(e\) given \(h\) is true, \(p(h)\) is the prior probability of hypothesis \(h\), \(p(e|\sim h)\) is the conditional probability of \(e\) given \(h\) is false, and \(p(\sim h)\) is the probability that the event \(h\) is not true, which, according to the formula for the total probability, can be calculated as:  
\[p(\sim h)=1-p(h), \tag{2}\]  
Thus, to calculate the conditional probability \(p(h|e)\), the probabilities \(p(e|h)\), \(p(e|\sim h)\), and the prior probability \(p(h)\) is are sufficient.