Sentiment visualization of correlation of loneliness mapped through social intelligence analysis
<h3>Background</h3><p dir="ltr">Loneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adult...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513519845638144 |
|---|---|
| author | Hurmat Ali Shah (18192889) |
| author2 | Marco Agus (8032898) Mowafa Househ (9154124) |
| author2_role | author author |
| author_facet | Hurmat Ali Shah (18192889) Marco Agus (8032898) Mowafa Househ (9154124) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hurmat Ali Shah (18192889) Marco Agus (8032898) Mowafa Househ (9154124) |
| dc.date.none.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.cmpbup.2024.100144 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Sentiment_visualization_of_correlation_of_loneliness_mapped_through_social_intelligence_analysis/25449574 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Materials engineering Loneliness Twitter data analysis Natural language processing Sentiment analysis Topic modeling Interactive visualization Tree map Radar plot |
| dc.title.none.fl_str_mv | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Loneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon.</p><h3>Objectives</h3><p dir="ltr">This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed.</p><h3>Methods</h3><p dir="ltr">This study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations.</p><h3>Results</h3><p dir="ltr">The analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue.</p><h3>Conclusion</h3><p dir="ltr">This paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Methods and Programs in Biomedicine Update<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cmpbup.2024.100144" target="_blank">https://dx.doi.org/10.1016/j.cmpbup.2024.100144</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d655323b4731d73c1827b7cbf200be62 |
| identifier_str_mv | 10.1016/j.cmpbup.2024.100144 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25449574 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sentiment visualization of correlation of loneliness mapped through social intelligence analysisHurmat Ali Shah (18192889)Marco Agus (8032898)Mowafa Househ (9154124)EngineeringMaterials engineeringLonelinessTwitter data analysisNatural language processingSentiment analysisTopic modelingInteractive visualizationTree mapRadar plot<h3>Background</h3><p dir="ltr">Loneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon.</p><h3>Objectives</h3><p dir="ltr">This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed.</p><h3>Methods</h3><p dir="ltr">This study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations.</p><h3>Results</h3><p dir="ltr">The analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue.</p><h3>Conclusion</h3><p dir="ltr">This paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Methods and Programs in Biomedicine Update<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cmpbup.2024.100144" target="_blank">https://dx.doi.org/10.1016/j.cmpbup.2024.100144</a></p>2024-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.cmpbup.2024.100144https://figshare.com/articles/journal_contribution/Sentiment_visualization_of_correlation_of_loneliness_mapped_through_social_intelligence_analysis/25449574CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/254495742024-01-01T00:00:00Z |
| spellingShingle | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis Hurmat Ali Shah (18192889) Engineering Materials engineering Loneliness Twitter data analysis Natural language processing Sentiment analysis Topic modeling Interactive visualization Tree map Radar plot |
| status_str | publishedVersion |
| title | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| title_full | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| title_fullStr | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| title_full_unstemmed | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| title_short | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| title_sort | Sentiment visualization of correlation of loneliness mapped through social intelligence analysis |
| topic | Engineering Materials engineering Loneliness Twitter data analysis Natural language processing Sentiment analysis Topic modeling Interactive visualization Tree map Radar plot |