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        <title>Aspect Based Sentiment Analysis of Finnish Neighborhoods: Insights from Suomi24</title>
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        <namePart type="given">Laleh</namePart>
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        <namePart type="family">Öörni</namePart>
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            <title>Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages</title>
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            <namePart type="given">Mika</namePart>
            <namePart type="family">Hämäläinen</namePart>
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            <namePart type="given">Flammie</namePart>
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            <namePart type="given">Melany</namePart>
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            <namePart type="given">Mario</namePart>
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    <abstract>This study presents an approach to Aspect-Based Sentiment Analysis (ABSA) using Natural Language Processing (NLP) techniques to explore public sentiment across 12 suburban neighborhoods in Finland. We employed and compared a range of machine learning models for sentiment classification, with the RoBERTa model emerging as the best performer. Using RoBERTa, we conducted a comprehensive sentiment analysis(SA) on a manually annotated dataset and a predicted dataset comprising 32,183 data points to investigate sentiment trends over time in these areas. The results provide insights into fluctuations in public sentiment, highlighting both the robustness of the RoBERTa model and significant shifts in sentiment for specific neighborhoods over time. This research contributes to a deeper understanding of neighborhood sentiment dynamics in Finland, with potential implications for social research and urban development.</abstract>
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%0 Conference Proceedings
%T Aspect Based Sentiment Analysis of Finnish Neighborhoods: Insights from Suomi24
%A Davoodi, Laleh
%A Öörni, Anssi
%A Harkke, Ville
%Y Hämäläinen, Mika
%Y Pirinen, Flammie
%Y Macias, Melany
%Y Crespo Avila, Mario
%S Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages
%D 2024
%8 November
%I Association for Computational Linguistics
%C Helsinki, Finland
%F davoodi-etal-2024-aspect
%X This study presents an approach to Aspect-Based Sentiment Analysis (ABSA) using Natural Language Processing (NLP) techniques to explore public sentiment across 12 suburban neighborhoods in Finland. We employed and compared a range of machine learning models for sentiment classification, with the RoBERTa model emerging as the best performer. Using RoBERTa, we conducted a comprehensive sentiment analysis(SA) on a manually annotated dataset and a predicted dataset comprising 32,183 data points to investigate sentiment trends over time in these areas. The results provide insights into fluctuations in public sentiment, highlighting both the robustness of the RoBERTa model and significant shifts in sentiment for specific neighborhoods over time. This research contributes to a deeper understanding of neighborhood sentiment dynamics in Finland, with potential implications for social research and urban development.
%U https://aclanthology.org/2024.iwclul-1.1
%P 1-11

Markdown (Informal)

[Aspect Based Sentiment Analysis of Finnish Neighborhoods: Insights from Suomi24](https://aclanthology.org/2024.iwclul-1.1) (Davoodi et al., IWCLUL 2024)

• Aspect Based Sentiment Analysis of Finnish Neighborhoods: Insights from Suomi24 (Davoodi et al., IWCLUL 2024)

ACL

• Laleh Davoodi, Anssi Öörni, and Ville Harkke. 2024. Aspect Based Sentiment Analysis of Finnish Neighborhoods: Insights from Suomi24. In Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages, pages 1–11, Helsinki, Finland. Association for Computational Linguistics.