Abstract
In this article the description of algorithm of an assessment of mood of the statement is presented with the accent on the context of user’s messages in social media. The article focuses on the fact that messages containing identical sentiment objects have different meaning that affects onto the evaluation of the sentiment of the message. An additional research objective is the identification of formal criteria for assigning messages to classes “core”, “periphery”, “non-relevant” to denote the role of the research relevance of the object key in the message. In this article, we have given several examples of authentic messages for each group.
The method was tested on the empirical basis of more than 10,000 messages to assess the relationship of users of the social network VKontakte to the object of tonality – a form of employment “freelance”. The research methodology presupposes the use of basic and additional methods of data preprocessing, data augmentation, comparative analysis of the application of classification methods. The article includes comparative description of results of application logistic regression, support vector machines, naive Bayesian classifier, nearest neighbor, random forest.
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Acknowledgements
This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.575.21.0178 (ID RFMEFI57518X0178).
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Maltseva, A.V., Makhnytkina, O.V., Shilkina, N.E., Lizunova, I.A. (2020). Social Media Sentiment Analysis with Context Space Model. In: Chugunov, A., Khodachek, I., Misnikov, Y., Trutnev, D. (eds) Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2019. Communications in Computer and Information Science, vol 1135. Springer, Cham. https://doi.org/10.1007/978-3-030-39296-3_29
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