TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

Hettiarachchi, Hansi and Adedoyin-Olowe, Mariam and Bhogal, Jagdev and Gaber, Mohamed Medhat (2022) TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments. Data, 7 (7). pp. 90-106. ISSN 2306-5729

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Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.

Item Type: Article
Identification Number: https://doi.org/10.3390/data7070090
23 June 2022Accepted
30 June 2022Published Online
Uncontrolled Keywords: event detection, sentiment analysis, aggregated sentiments, Twitter, ensembled data annotation
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Jagdev Bhogal
Date Deposited: 01 Dec 2022 15:04
Last Modified: 01 Dec 2022 15:04
URI: https://www.open-access.bcu.ac.uk/id/eprint/13968

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