Journal article
Aspect sentiment mining of short bullet screen comments from online TVseries
Journal of the Association for Information Science and Technology, Vol.74(8), pp.1026-1045
08/2023
DOI: 10.1002/asi.24800
Abstract
Bullet screen comments (BSCs) are user-generated short comments that appear as real-time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect-level sentiment analysis framework. Our framework, BSCNET, is a pre-trained language encoder-based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi-supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine-tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%–19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect-level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.
Details
- Title: Subtitle
- Aspect sentiment mining of short bullet screen comments from online TVseries
- Creators
- Jiayue Liu - Dongbei University of Finance and EconomicsZiyao Zhou - Dongbei University of Finance and EconomicsMing Gao - Dongbei University of Finance and EconomicsJiafu Tang - Dongbei University of Finance and EconomicsWeiguo Fan - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Association for Information Science and Technology, Vol.74(8), pp.1026-1045
- DOI
- 10.1002/asi.24800
- ISSN
- 2330-1635
- eISSN
- 2330-1643
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 71772033, 72293563, 71831003; DOI: 10.13039/501100005047, name: Natural Science Foundation of Liaoning Province, award: 2020‐KF‐11‐11; DOI: 10.13039/501100011991, name: Dongbei University of Finance and Economics, award: PT‐Y202214
- Language
- English
- Date published
- 08/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984444761202771
Metrics
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