Details
| Original language | English |
|---|---|
| Title of host publication | WWW '21 |
| Subtitle of host publication | Companion Proceedings of the Web Conference 2021 |
| Pages | 166-170 |
| Number of pages | 5 |
| ISBN (electronic) | 9781450383134 |
| Publication status | Published - 2021 |
| Event | World Wide Web Conference (WWW 2021) - Ljubljana, Slovenia Duration: 19 Apr 2021 → 23 Apr 2021 Conference number: 30 |
Abstract
Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-Trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.
Keywords
- BERT, BertViz, Migration, RoBERTa, Twitter
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW '21: Companion Proceedings of the Web Conference 2021. 2021. p. 166-170.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analyzing European Migrant-related Twitter Deliberations
AU - Khatua, Aparup
AU - Nejdl, Wolfgang
N1 - Conference code: 30
PY - 2021
Y1 - 2021
N2 - Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-Trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.
AB - Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-Trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.
KW - BERT
KW - BertViz
KW - Migration
KW - RoBERTa
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85107649091&partnerID=8YFLogxK
U2 - 10.1145/3442442.3453459
DO - 10.1145/3442442.3453459
M3 - Conference contribution
AN - SCOPUS:85107649091
SP - 166
EP - 170
BT - WWW '21
T2 - World Wide Web Conference (WWW 2021)
Y2 - 19 April 2021 through 23 April 2021
ER -