The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.

Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts.

Nowadays, product reviews on e-commerce sites tend to be a valuable resource in terms of evaluation of customers' behavior, their preferences, and needs. This paper provides an approach for sentiment analysis of product reviews in Russian using convolutional neural networks. We use Word2Vec pre-trained vectors as inputs for neural networks. This approach utilizes no hand-crafted features or sentiment lexicons. The training dataset was collected from reviews on top-ranked goods from the major e-commerce site in Russia, where the user-ranked scores were used as class labels. The system demonstrated the F-measure score up to 75.45% in a three-class classification. The collected training dataset and word embeddings are available to the research community.

The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into five classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F₁ = 0.6594 on the test subset.

A growing body of literature has examined the potential of machine learning algorithms in constructing social indicators based on the automatic classification of digital traces. However, as long as the classification algorithms’ predictions are not completely error-free, the estimate of the relative occurrence of a particular class may be affected by misclassification bias, thereby affecting the value of the calculated social indicator. Although a significant amount of studies have investigated misclassification bias correction techniques, they commonly rely on a set of assumptions that are likely to be violated in practice, which calls into question the effectiveness of these methods. Thus, there is a knowledge gap with respect to the assessment of misclassification bias’s impact on a specific social indicator formula without strict reference to the number of classes. Moreover, given the erroneous nature of automatic classification algorithms, the quality of a predicted indicator can be assessed not only using regression quality metrics, as was done in existing literature, but also using correlation metrics. In this paper, we propose a simulation approach for assessing the impact of misclassification bias on the calculated social indicators in terms of regression and correlation metrics. The proposed approach focuses on indicators calculated based on the distribution of classes and can process any number of classes. The proposed approach allows selecting the most appropriate classification model for a particular social indicator, and vice versa. Moreover, it allows for assessment of the optimistic level of correlation between the indicator calculated based on the results of the classification algorithm and the true underlying indicator.