LDA model tuning for the "Pandemic" subset.
<div><p>Purpose</p><p>The COVID-19 pandemic has changed customer social media engagement behavior, which challenges the establishment of effective marketing strategies to strengthen digital communication with customers and leads to new opportunities for social media competiti...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852024937454239744 |
|---|---|
| author | Xingting Ju (20323838) |
| author_facet | Xingting Ju (20323838) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xingting Ju (20323838) |
| dc.date.none.fl_str_mv | 2024-11-25T19:08:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313191.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/LDA_model_tuning_for_the_Pandemic_subset_/27902915 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Pharmacology Evolutionary Biology Developmental Biology Science Policy Biological Sciences not elsewhere classified used amazon comprehend strengthen digital communication predicting customer engagement latent dirichlet allocation effective marketing strategies dual focus provides collected tweets generated based empirical study performance metrics show include &# 8220 customer engagement prediction brand topic identification c50 performs best &# 8220 study presents brand topics xlink "> topics expanded topic detection random forest proposed framework performing models particularly strong new opportunities framework differentiates extract sentiments external factors comprehensive approach |
| dc.title.none.fl_str_mv | LDA model tuning for the "Pandemic" subset. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Purpose</p><p>The COVID-19 pandemic has changed customer social media engagement behavior, which challenges the establishment of effective marketing strategies to strengthen digital communication with customers and leads to new opportunities for social media competitive intelligence analytics. This study presents a new social media competitive intelligence framework that incorporates not only the detection of brand topics before and during the COVID-19 pandemic but also the prediction of customer engagement.</p><p>Design/Methodology/Approach</p><p>A sector-based empirical study is conducted to illustrate the implementation of the proposed framework. We collected tweets generated by 23 leading American catering brands before and during the pandemic. First, we used Amazon Comprehend and Latent Dirichlet allocation (LDA) to extract sentiments and topics behind unstructured text data. Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. Pre-pandemic topics primarily included “Food and lifestyle”, “Promotion”, “Food ordering”, “Food time”, and “Food delivery”. During the pandemic, the topics expanded to include “Social responsibility” and “Contactless ordering”. For predicting customer engagement, the performance metrics show that Random Forest and C5.0 (C50) are generally the best-performing models, with Random Forest being particularly strong for "Likes" and “Retweets”, while C50 performs best for “Replies”.</p><p>Originality</p><p>This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_fcff87bdf895ff20ea48fe61914d25a2 |
| identifier_str_mv | 10.1371/journal.pone.0313191.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27902915 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LDA model tuning for the "Pandemic" subset.Xingting Ju (20323838)MedicinePharmacologyEvolutionary BiologyDevelopmental BiologyScience PolicyBiological Sciences not elsewhere classifiedused amazon comprehendstrengthen digital communicationpredicting customer engagementlatent dirichlet allocationeffective marketing strategiesdual focus providescollected tweets generatedbased empirical studyperformance metrics showinclude &# 8220customer engagement predictionbrand topic identificationc50 performs best&# 8220study presentsbrand topicsxlink ">topics expandedtopic detectionrandom forestproposed frameworkperforming modelsparticularly strongnew opportunitiesframework differentiatesextract sentimentsexternal factorscomprehensive approach<div><p>Purpose</p><p>The COVID-19 pandemic has changed customer social media engagement behavior, which challenges the establishment of effective marketing strategies to strengthen digital communication with customers and leads to new opportunities for social media competitive intelligence analytics. This study presents a new social media competitive intelligence framework that incorporates not only the detection of brand topics before and during the COVID-19 pandemic but also the prediction of customer engagement.</p><p>Design/Methodology/Approach</p><p>A sector-based empirical study is conducted to illustrate the implementation of the proposed framework. We collected tweets generated by 23 leading American catering brands before and during the pandemic. First, we used Amazon Comprehend and Latent Dirichlet allocation (LDA) to extract sentiments and topics behind unstructured text data. Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. Pre-pandemic topics primarily included “Food and lifestyle”, “Promotion”, “Food ordering”, “Food time”, and “Food delivery”. During the pandemic, the topics expanded to include “Social responsibility” and “Contactless ordering”. For predicting customer engagement, the performance metrics show that Random Forest and C5.0 (C50) are generally the best-performing models, with Random Forest being particularly strong for "Likes" and “Retweets”, while C50 performs best for “Replies”.</p><p>Originality</p><p>This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence.</p></div>2024-11-25T19:08:05ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0313191.g005https://figshare.com/articles/figure/LDA_model_tuning_for_the_Pandemic_subset_/27902915CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279029152024-11-25T19:08:05Z |
| spellingShingle | LDA model tuning for the "Pandemic" subset. Xingting Ju (20323838) Medicine Pharmacology Evolutionary Biology Developmental Biology Science Policy Biological Sciences not elsewhere classified used amazon comprehend strengthen digital communication predicting customer engagement latent dirichlet allocation effective marketing strategies dual focus provides collected tweets generated based empirical study performance metrics show include &# 8220 customer engagement prediction brand topic identification c50 performs best &# 8220 study presents brand topics xlink "> topics expanded topic detection random forest proposed framework performing models particularly strong new opportunities framework differentiates extract sentiments external factors comprehensive approach |
| status_str | publishedVersion |
| title | LDA model tuning for the "Pandemic" subset. |
| title_full | LDA model tuning for the "Pandemic" subset. |
| title_fullStr | LDA model tuning for the "Pandemic" subset. |
| title_full_unstemmed | LDA model tuning for the "Pandemic" subset. |
| title_short | LDA model tuning for the "Pandemic" subset. |
| title_sort | LDA model tuning for the "Pandemic" subset. |
| topic | Medicine Pharmacology Evolutionary Biology Developmental Biology Science Policy Biological Sciences not elsewhere classified used amazon comprehend strengthen digital communication predicting customer engagement latent dirichlet allocation effective marketing strategies dual focus provides collected tweets generated based empirical study performance metrics show include &# 8220 customer engagement prediction brand topic identification c50 performs best &# 8220 study presents brand topics xlink "> topics expanded topic detection random forest proposed framework performing models particularly strong new opportunities framework differentiates extract sentiments external factors comprehensive approach |