The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Smetanin, Sergey

doi:10.1109/access.2020.3002215

Cited by 58 publications

(31 citation statements)

References 140 publications

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“…After that, we can calculate T SI T , T SI P , and qm i . By repeating the entire procedure multiple times 5 , we can obtain multiple qm i and calculate aqm i .…”

Section: B Predicted Indicator Approximationmentioning

confidence: 99%

“…These characteristics make digital traces an ideal source for building social indicators defined by Ferriss [3] as statistical time series "used to monitor the social system, helping to identify changes and to guide intervention to alter the course of social change." A typical example of social indicators constructed using ML analysis is the estimation of subjective well-being (SWB) based on user-generated content from social media, by employing an ML model trained to classify the sentiment of posts [4], [5]. From a practical point of view, a classification algorithm is commonly used to classify digital traces to the classes of interest; then, based on the distribution of these classes, an VOLUME 4, 2016 indicator is calculated for the entire population [6].…”

Section: Introductionmentioning

confidence: 99%

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Misclassification Bias in Computational Social Science: A Simulation Approach for Assessing the Impact of Classification Errors on Social Indicators Research

Smetanin

Komarov

2022

IEEE Access

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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.

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“…After that, we can calculate T SI T , T SI P , and qm i . By repeating the entire procedure multiple times 5 , we can obtain multiple qm i and calculate aqm i .…”

Section: B Predicted Indicator Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Misclassification Bias in Computational Social Science: A Simulation Approach for Assessing the Impact of Classification Errors on Social Indicators Research

Smetanin

Komarov

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

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“…In their studies, various techniques for the emotional analysis of social media data were examined. Additionally, in the literature, sensitivity changes (Liang et al, 2020;Mukherjee et al, 2021), commercial efficiency analyses (Smetanin, 2020) and semantic fuzziness analyzes (Fang et al, 2018) was handled as a sentiment analysis problem.…”

Section: Background and Related Workmentioning

confidence: 99%

A new plant intelligence-based method for sentiment analysis: Chaotic sunflower optimization

Yildirim¹,

Yıldırım²,

Alataş³

2021

Computer Science

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Various social networking applications provide people with many opportunities such as expressing, commenting, disseminating and transmitting their opinions within certain limits. The emotions and ideas that people express in their messages make sense of thousands of articles and opinions published instantly. Trying to make sense of emotional data, generating meaningful information from these data, analyzing these data, and making predictions and inferences on these data is a new important study field. In this study, sentiment analysis is considered an optimization problem in order to achieve high performance. For this purpose, sunflower optimization, which is one of the new and successful plant intelligence-based algorithms, has been modelled as a sentiment analyzer for the first time. A chaotic sunflower optimization algorithm was used by combining sunflower optimization and chaos theory in order to make effective sentiment analysis. In order for the proposed method to effectively solve the sentiment analysis problem, a suitable representation form and fitness function have been proposed. The proposed method treats the data as a search space and searches for a solution for analysis by detecting emotion in this search space. An up-to-date data set including customer feedback and satisfaction information was used in the study. Results based on accuracy, precision, and recall metrics show that plant intelligence-based metaheuristic algorithms can provide high performance.

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“…There are currently five sources of textual data: user-generated content, product and service reviews, news content, books, and mixed data. In a review paper on applied sentiment analysis [21], it was noted that for the Russian language, this field is little explored (the author notes the 27 most relevant studies on the analysis of sentiments in Russian). Much of the research has focused on analyzing the sentiments of tweets (short messages) on the social network Twitter.…”

Section: Introductionmentioning

confidence: 99%

The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

et al. 2021

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Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks.

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The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Cited by 58 publications

References 140 publications

Misclassification Bias in Computational Social Science: A Simulation Approach for Assessing the Impact of Classification Errors on Social Indicators Research

Misclassification Bias in Computational Social Science: A Simulation Approach for Assessing the Impact of Classification Errors on Social Indicators Research

A new plant intelligence-based method for sentiment analysis: Chaotic sunflower optimization

The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

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