From Sentiment to Strategy: Emotional Classification of Customer Engagement in Corporate Cosmetic Video Marketing

This study examines various emotions expressed in customer engagement content available in corporate product videos on the TikTok platform. The research identified 301 posts with a total of 39,615 comment data collected from the official TikTok account. The data underwent a preprocessing stage to ensure accurate processing, followed by stemming, classification using TF-IDF, Bag-of-Words visualization, and classification with the NRC EmoLex library to detect emotions present in the dataset. The findings reveal that, in addition to positive and negative comments, various emotions such as Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Positive, and Negative were identified. The highest emotion score was Positive (33%), followed by Joy (17%), Anticipation (16%), and Surprise (12%). These results indicate that the content analyzed in this study has a substantial positive impact on the audience, with positive emotions dominating. However, companies should remain attentive to the presence of Anticipation and Negative emotions. Therefore, it is essential to maintain factors that trigger positive emotions, minimize content that elicits negative emotions, and manage sensitive content carefully to avoid generating adverse responses.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-07
DOI
https://doi.org/10.5281/zenodo.17292376
Primary Topic
Middle East and Rwanda Conflicts
Type
article
Field-Weighted Citation Impact
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article

From Sentiment to Strategy: Emotional Classification of Customer Engagement in Corporate Cosmetic Video Marketing

Zenodo (CERN European Organization for Nuclear Research)
Middle East and Rwanda Conflicts
article

From Sentiment to Strategy: Emotional Classification of Customer Engagement in Corporate Cosmetic Video Marketing

article en

Abstract

This study examines various emotions expressed in customer engagement content available in corporate product videos on the TikTok platform. The research identified 301 posts with a total of 39,615 comment data collected from the official TikTok account. The data underwent a preprocessing stage to ensure accurate processing, followed by stemming, classification using TF-IDF, Bag-of-Words visualization, and classification with the NRC EmoLex library to detect emotions present in the dataset. The findings reveal that, in addition to positive and negative comments, various emotions such as Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Positive, and Negative were identified. The highest emotion score was Positive (33%), followed by Joy (17%), Anticipation (16%), and Surprise (12%). These results indicate that the content analyzed in this study has a substantial positive impact on the audience, with positive emotions dominating. However, companies should remain attentive to the presence of Anticipation and Negative emotions. Therefore, it is essential to maintain factors that trigger positive emotions, minimize content that elicits negative emotions, and manage sensitive content carefully to avoid generating adverse responses.

Zenodo (CERN European Organization for Nuclear Research)
Universitas Islam Negeri Sultan Syarif Kasim Riau (ID)
Openalex Percentile: Top 37%
Middle East and Rwanda Conflicts
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From Sentiment to Strategy: Emotional Classification of Customer Engagement in Corporate Cosmetic Video Marketing · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS