Sentiment Analysis of Posts and Comments in the Accounts of Russian Politicians on the Social Network

Svetlov, Kirill; Platonov, Konstantin

doi:10.23919/fruct48121.2019.8981501

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…Deleting abusive words completely would deprive the data of potential analyses opportunities, and hence a specifically coded algorithm was used to make a customized replacement. This customized replacement was in addition to the standard use of "Stopwords" and cleaning processes [48,49]. The dataset was further evaluated to identify the most useful variables, and sixty two variables with incomplete, blank and irrelevant values were deleted to create a cleaned dataset with twenty eight variables.…”

Section: Data Acquisition and Preparationmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Section: Data Acquisition and Preparationmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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“…Deleting abusive words completely would deprive the data of potential analyses opportunities, and hence a specifically coded algorithm was used to make a customized replacement. This customized replacement was in addition to the standard use of “Stopwords” and cleaning processes [51,52]. The dataset was further evaluated to identify the most useful variables, and sixty two variables with incomplete, blank and irrelevant values were deleted to create a cleaned dataset with twenty eight variables.…”

Section: Methods and Textual Data Analyticsmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Samuel

Ali

Rahman

et al. 2020

Preprint

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91\% for short Tweets, with the Na\"ive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74\% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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“…Besides, some studies (e.g. [69]) also examined patterns of user interaction with content depending on its sentiments. One of the critical challenges of these studies was to retrieve a representative data sample and to filter relevant texts for the analysis.…”

Section: The Applications Of Sentiment Analysis For Russian Langumentioning

confidence: 99%

“…Content from social networks can be a valuable source of information not only about the attitudes towards different topics but also about user behaviour patterns during interaction with the content. Svetlov and Platonov determined the impact of sentiment on the mechanisms of feedback from the audience [69]. As a source of data, they utilised 46,293 posts and 2,197,063 comments from the most popular accounts of Russian politicians on the social network VKontakte in the period from January 2017 to April 2019.…”

Section: ) User Behaviourmentioning

confidence: 99%

“…1) Transfer learning of language models for sentiment analysis. The majority of analysed papers applied rulebased and basic machine learning approaches, and only two studies [69], [72] utilised neural networks. However, recent studies showed that transfer learning from pre-trained language models have proven to be effective in the sentiment classification task, confidently achieving strong results [43], [195]- [199].…”

Section: Future Research Opportunitiesmentioning

confidence: 99%

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

Smetanin

2020

IEEE Access

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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. INDEX TERMS Classification, machine learning, computational linguistics, sentiment analysis, applications of sentiment analysis, Russian-language texts, public opinion.

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Sentiment Analysis of Posts and Comments in the Accounts of Russian Politicians on the Social Network

Cited by 18 publications

References 22 publications

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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

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