Research Library

open-access-imgOpen AccessExploiting User-Generated Content in Product Launch Videos to Compute a Launch Score
Author(s)
Sibanjan Debeeprasad Das,
Pradip Kumar Bala,
Sukanta Das
Publication year2024
Publication title
ieee access
Resource typeMagazines
PublisherIEEE
This study investigated the relationship between the essential aspects of user-generated content (UGC) and product launch videos to derive the product launch score (PLS). This score can be considered a key performance indicator (KPI) to evaluate the performance of product launch videos. The product launch score can provide businesses and marketers with insights into how well the community and audience perceive a product launch on virtual social media platforms such as YouTube. The authors examined 52 product launch videos with a total of 1,11,716 comments on YouTube and analyzed the data to derive various sentimental, emotional, and social networking aspects from the comments on the product launch videos. Furthermore, the relationship between brand and product mentions was evaluated to determine the centrality of the launch activity. The work determined how effectively the community was engaged with the brand and product launch. Finally, relationship analysis and principal component analysis (PCA) were performed to select relevant aspects for calculating the PLS. This KPI provides a holistic view of user engagement in product launch videos.
Subject(s)aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Keyword(s)Social networking (online), Web sites, Video on demand, Videos, Companies, Key performance indicator, User-generated content, Text mining, Emotion recognition, Analytical models, Market research, Text Mining, Social Networks, Emotions Analysis, Word-of-Mouth, Analytic models, Marketing Analytics
Language(s)English
SCImago Journal Rank0.587
H-Index127
eISSN2169-3536
DOI10.1109/access.2024.3381541

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