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During the last decade a lot of academic papers consider the possibility of predicting the economic fluctuations and macroeconomic variables volatility with the use of news data. The reason for this is the development of new machine learning techniques and enhancement of the existed methods. The scientific problem of our study is the investigation of whether predictive power of the forecast of macroeconomic variables can be improved with the use of news data in the context of Russia. We apply NLU algorithms and techniques for topic modeling. Especially, we implement LDA (Latent Dirichlet Allocation) since this approach has shown its effectiveness in the published papers related to the mentioned framework. Then the frequency news and sentiment news indexes are constructed with the use of modeled topics. The end point of our research is the forecast analysis of the set of macroeconomics variables [CPI (π), Business Confidence Index (BCI), Consumer Confidence Index (CCI), Export (EX), Import (IM), Net Export (NX)] supplemented by inclusion of frequency and sentiment news indexes in order to evaluated the improvement in predictive power. We have shown that the inclusion of frequency news indexes and sentiment news indexes, based on the LDA approach in the forecast models can improve the quality of the predictions and increase the predictive power for some variables.

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Depression is a global mental health issue generalized by a decrease in mood and satisfaction. Treatments for individuals afflicted with depressive symptoms include prescribed medications that require diagnosis to acquire. The purpose of this investigation was to accurately assist psychiatrists in diagnosis procedures to prevent both false positive and false negative conclusions by utilizing machine learning on social media messages. This was done by training a machine learning algorithm which accurately predicted and detected depressive behaviors and communications. As social media messages encompass individual’s general communications among long periods of time with high consistency and frequency, I hypothesized that social media messages could be used as a method to both train an accurate and consistent machine learning model for the detection of depression. Social media dataset messages rely on self-reported diagnoses. Based on F1 accuracy normalization across machine learning HyperTuning, average accuracy indicated 97% [+/-0.5%] among a ~7600 sample dataset. Utilizing generalized sentimental analysis has shown less accurate results (~80%) but needs further research.

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Humor is an important part of personal communication. How to create a computational model to recognize humor is still a very challenging task in natural language processing and linguistics. In this survey, we applied some rules to leave 29 articles spanning 10 years (2012 to 2023). The main elements covered by this survey include: recent state-of-the-art detection methods using deep learning from years 2012-2023, (2) summarizing features for humor detection from a linguistic perspective, (3) humor detection datasets, evaluation metrics, data domains and languages, (4) some tricks used in humor detection (e.g. Attention mechanism, multimodal), (5) recognizing open problems and highlight the feasible opportunities for future research directions. To the best of our knowledge, this is the first systematic survey for humor detection using deep learning. The survey can be used to assist novice and prominent researchers to understand the concept of humor, popular method and future research direction and so on.

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