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The article deals with the relevant problem of automatic arbitrary text summary obtaining. Several works in this field are analyzed using comparison and classification methods. The problem of obtaining a text summary by means of an answer to a random question is emphasized. The identification of semantic relations between sentences using a set of rules based on syntax and semantics of a language is described. These rules are represented in the form of regular expressions – patterns that consist of characters and metacharacters and set search rules. Taking into account semantic coherence features, an improved method of sentences similarity calculation to identify the measure of inclusion of one sentence into another one is developed. This method helps to define more precisely logical stress on the words within automatic summarization and detect contradictions. A modified automatic summarization method, oriented at a specific problem is suggested. It is concluded that the proposed method is quiet effective in the process of automatic search for answers to questions in the Internet.

Multimodal Sentiment Analysis (MSA) is a growing area of emotional computing that involves analyzing data from three different modalities. Gathering data from Multimodal Sentiment analysis in Car Reviews (MuSe-CaR) is challenging due to data imbalance across modalities. To address this, an effective data augmentation approach is proposed by combining dynamic synthetic minority oversampling with a multimodal elicitation conditional generative adversarial network for emotion recognition using audio, text, and visual data. The balanced data is then fed into a granular elastic-net regression with a hybrid feature selection method based on dandelion fick’s law optimization to analyze sentiments. The selected features are input into a multilabel wavelet convolutional neural network to classify emotion states accurately. The proposed approach, implemented in python, outperforms existing methods in terms of trustworthiness (0.695), arousal (0.723), and valence (0.6245) on the car review dataset. Additionally, the feature selection method achieves high accuracy (99.65%), recall (99.45%), and precision (99.66%). This demonstrates the effectiveness of the proposed MSA approach, even with three modalities of data.

Depression is a prevalent mental condition, and it is essential to diagnose and treat patients as soon as possible to maximize their chances of rehabilitation and recovery. An intelligent detection model based on multimodal fusion technology is proposed based on the findings of this study to address the difficulties associated with depression detection. Text data and electroencephalogram (EEG) data are used in the model as representatives of subjective and objective nature, respectively. These data are processed by the BERT–TextCNN model and the CNN–LSTM model, which are responsible for processing them. While the CNN–LSTM model is able to handle time-series data in an effective manner, the BERT–TextCNN model is able to adequately capture the semantic information that is included in text data. This enables the model to consider the various features that are associated with the various types of data. In this research, a weighted fusion technique is utilized to combine the information contained within the two modal datasets. This strategy involves assigning a weight to the outcomes of each modal data processing in accordance with the degree of contribution that each modal data will make to produce the ultimate depression detection results. In regard to the task of depression identification, the suggested model demonstrates great validity and robustness, as demonstrated by the results of the experimental validation that we carried out on a dataset that we manufactured ourselves. A viable and intelligent solution for the early identification of depression is provided by the proposed model. This solution will likely be widely utilized in clinical practice and will provide new ideas and approaches for the growth of the field of precision medicine.