Emotion Detection Based on Sentiment Analysis: An Example of a Social Robots on Short and Long Texts Conversation

Eryka Probierz and Adam Galuszka

European Research Studies Journal, 2022, vol. XXV, issue 2, 135-144

Abstract: Purpose: The aim of this paper is to present a solution to detect emotions from text obtained in a conversation with a social robot. Emotions will be detected using sentiment analysis based on the English and Polish lexicon. Design/methodology/approach: Data from social robot conversation records will be converted into text and then split into short and long speech. The original language utterances will then be analysed using the Polish lexicon, while the translated texts will be analysed using the English emotional lexicon. Findings: The results obtained indicate the same or similar distribution of emotions made by sentiment analysis using both plNetWord and NRC lexicons. Practical Implications: The results obtained can be used for further research addressing the creation and development of lexicons based on the selected language. They are also applicable to the implementation of solutions for detecting and responding to conversational emotions by social robots. Originality/Value: The analyses so far mostly take up the subject of textual analysis in English. The aim of the present study is to analyse a Polish text and to compare the results obtained with those for English texts. The analysis of differences in the emotional sentiment of utterances may lead to the construction of more effective models based on the chosen language.

Keywords: Sentiment analysis; emotion detection; social robots. (search for similar items in EconPapers)
JEL-codes: D12 D47 D53 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxv:y:2022:i:2:p:135-144

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