Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach
Investors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.884699/full |
| _version_ | 1828107305856532480 |
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| author | Rongjiao Ji Qiwei Han |
| author_facet | Rongjiao Ji Qiwei Han |
| author_sort | Rongjiao Ji |
| collection | DOAJ |
| description | Investors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This paper explicitly considers how investor sentiment about financial market events is shaped by the relative discussions of different types of investors. We leverage a large-scale financial social media dataset and employ a structural topic modeling approach to extract topical contents of investor sentiment across multiple finance-specific factors. The identified topics reveal important events related to the financial market and show strong heterogeneity in the social media content in terms of compositions of investor profiles, asset categories, and bullish/bearish sentiment. Results show that investors with different profiles and trading preferences tend to discuss financial markets with heterogeneous beliefs, leading to divergent opinions about those events regarding the topic prevalence and proportion. Moreover, our findings may shed light on the mechanism that underlies the efficient investor sentiment extraction and aggregation while considering the heterogeneity of investor sentiment across different dimensions. |
| first_indexed | 2024-04-11T10:29:07Z |
| format | Article |
| id | doaj.art-55957ee181724952a5c51cbbb43128de |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-11T10:29:07Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-55957ee181724952a5c51cbbb43128de2022-12-22T04:29:28ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-10-01510.3389/frai.2022.884699884699Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approachRongjiao Ji0Qiwei Han1Department of Mathematics, University of Milan, Milan, ItalyNova School of Business and Economics, Universidade NOVA de Lisboa, Lisbon, PortugalInvestors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This paper explicitly considers how investor sentiment about financial market events is shaped by the relative discussions of different types of investors. We leverage a large-scale financial social media dataset and employ a structural topic modeling approach to extract topical contents of investor sentiment across multiple finance-specific factors. The identified topics reveal important events related to the financial market and show strong heterogeneity in the social media content in terms of compositions of investor profiles, asset categories, and bullish/bearish sentiment. Results show that investors with different profiles and trading preferences tend to discuss financial markets with heterogeneous beliefs, leading to divergent opinions about those events regarding the topic prevalence and proportion. Moreover, our findings may shed light on the mechanism that underlies the efficient investor sentiment extraction and aggregation while considering the heterogeneity of investor sentiment across different dimensions.https://www.frontiersin.org/articles/10.3389/frai.2022.884699/fullinvestor sentimentstructural topic modelingtext miningsocial mediaunstructured data analysis |
| spellingShingle | Rongjiao Ji Qiwei Han Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach Frontiers in Artificial Intelligence investor sentiment structural topic modeling text mining social media unstructured data analysis |
| title | Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach |
| title_full | Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach |
| title_fullStr | Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach |
| title_full_unstemmed | Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach |
| title_short | Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach |
| title_sort | understanding heterogeneity of investor sentiment on social media a structural topic modeling approach |
| topic | investor sentiment structural topic modeling text mining social media unstructured data analysis |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.884699/full |
| work_keys_str_mv | AT rongjiaoji understandingheterogeneityofinvestorsentimentonsocialmediaastructuraltopicmodelingapproach AT qiweihan understandingheterogeneityofinvestorsentimentonsocialmediaastructuraltopicmodelingapproach |