Abstract
The research is made in the frame of methodology of emotional text analysis – the computer technology allowing to classify texts according to the criterion of the emotion expressed in them. The paper focuses on specificity of verbal means used in texts of eight emotional classes to deal with the category of time. The data we used consist in 3900 Internet-texts from social network VKontakte assessed by 2000 informants on a crowdsource platform. We processed the raw data using the elements of mathematical modelling, and, a set of tools offered by Sketch Engine corpus manager platform. The hypothesis is that while experiencing an emotion, an individual feels the time differently and, consequently, speaks about it not in the same way. We paid a particular attention to the weight of the lexemes those semantics is connected with the idea of time in different emotional text classes.
Highlights
The research we conduct is focused on the problem of automatic text classification according the emotion expressed in it
Using the approach of supervised machine learning we need a training set. To collect it we retrieved 15000 posts from Russian social network VKontakte using the thematic hashtags containing key words of different emotions, later 3900 randomly taken texts from the collection were assessed by 2000 Russian informant on the one of crowd sourcing platform
Of the paper (§ 2), we examine the main assumptions of such approach as emotional text analysis, the concept of time as it is perceived in modern studies on semantics and cognitive linguistics and we describe the background of the project in general
Summary
The research we conduct is focused on the problem of automatic text classification according the emotion expressed in it. Using the approach of supervised machine learning we need a training set To collect it we retrieved 15000 posts from Russian social network VKontakte using the thematic hashtags containing key words of different emotions, later 3900 randomly taken texts from the collection were assessed by 2000 Russian informant on the one of crowd sourcing platform. In this way, we obtained a rather representative corpus of textual data available to the linguistic analysis. (§5 ) we summarize suggest how the obtained results can be further broadened
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