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16 results on '"Dash, Amanda"'

1. A Data-Driven Approach for Finding Requirements Relevant Feedback from TikTok and YouTube

Author

Sihag, Manish, Li, Ze Shi, Dash, Amanda, Arony, Nowshin Nawar, Devathasan, Kezia, Ernst, Neil, Albu, Alexandra, and Damian, Daniela

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Software Engineering
Abstract

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.

Published
2023

3. Texture-Based Data Augmentation for Small Datasets

Author

Dash, Amanda, Albu, Alexandra Branzan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Blanc-Talon, Jaques, editor, Delmas, Patrice, editor, Philips, Wilfried, editor, and Scheunders, Paul, editor

Published
2023
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7. WEATHERGOV+

Author

Dash, Amanda, primary, Cote, Melissa, additional, and Albu, Alexandra Branzan, additional

Published
2023
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8. Counting Large Flocks of Birds Using Videos Acquired with Hand-Held Devices

Author

Dash, Amanda, Albu, Alexandra Branzan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Blanc-Talon, Jacques, editor, Penne, Rudi, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2017
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9. A Domain Independent Approach to Video Summarization

Author

Dash, Amanda, Albu, Alexandra Branzan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Blanc-Talon, Jacques, editor, Penne, Rudi, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2017
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11. Beyond Traditional Feedback Channels: Extracting Requirements-Relevant Feedback from TikTok and YouTube

Author

Sihag, Manish, Li, Ze Shi, Dash, Amanda, Arony, Nowshin Nawar, Devathasan, Kezia, Ernst, Neil, Albu, Alexandra, and Damian, Daniela

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Human-Computer Interaction, Human-Computer Interaction (cs.HC)
Abstract

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.

Published
2023
Full Text
View/download PDF

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