Deep Learning-Based Event Prediction for Text Analysis

Muhammad Waseem; Qasim Umer; Choonhwa Lee; Sungwook Chung; Zohaib Latif

doi:10.1109/ICTC58733.2023.10392730

Deep Learning-Based Event Prediction for Text Analysis

Muhammad Waseem^*
, Qasim Umer
, Choonhwa Lee
, Sungwook Chung
, Zohaib Latif

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2% accuracy.

Original language	English
Title of host publication	ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Exploring the Frontiers of ICT Innovation
Publisher	IEEE Computer Society
Pages	42-47
Number of pages	6
ISBN (Electronic)	9798350313277
DOIs	https://doi.org/10.1109/ICTC58733.2023.10392730
State	Published - 2023
Externally published	Yes
Event	14th International Conference on Information and Communication Technology Convergence, ICTC 2023 - Jeju Island, Korea, Republic of Duration: 11 Oct 2023 → 13 Oct 2023

Publication series

Name	International Conference on ICT Convergence
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	14th International Conference on Information and Communication Technology Convergence, ICTC 2023
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/10/23 → 13/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Deep Learning
Event Prediction
Sentiment

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC58733.2023.10392730

Cite this

Waseem, M., Umer, Q., Lee, C., Chung, S., & Latif, Z. (2023). Deep Learning-Based Event Prediction for Text Analysis. In ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation (pp. 42-47). (International Conference on ICT Convergence). IEEE Computer Society. https://doi.org/10.1109/ICTC58733.2023.10392730

@inproceedings{faf7648b20f9440b85783104c4cfe7be,

title = "Deep Learning-Based Event Prediction for Text Analysis",

abstract = "This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2\% accuracy.",

keywords = "Deep Learning, Event Prediction, Sentiment",

author = "Muhammad Waseem and Qasim Umer and Choonhwa Lee and Sungwook Chung and Zohaib Latif",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information and Communication Technology Convergence, ICTC 2023 ; Conference date: 11-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/ICTC58733.2023.10392730",

language = "English",

series = "International Conference on ICT Convergence",

publisher = "IEEE Computer Society",

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booktitle = "ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence",

address = "United States",

}

Waseem, M, Umer, Q, Lee, C, Chung, S & Latif, Z 2023, Deep Learning-Based Event Prediction for Text Analysis. in ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. International Conference on ICT Convergence, IEEE Computer Society, pp. 42-47, 14th International Conference on Information and Communication Technology Convergence, ICTC 2023, Jeju Island, Korea, Republic of, 11/10/23. https://doi.org/10.1109/ICTC58733.2023.10392730

Deep Learning-Based Event Prediction for Text Analysis. / Waseem, Muhammad; Umer, Qasim; Lee, Choonhwa et al.
ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. IEEE Computer Society, 2023. p. 42-47 (International Conference on ICT Convergence).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning-Based Event Prediction for Text Analysis

AU - Waseem, Muhammad

AU - Umer, Qasim

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AU - Chung, Sungwook

AU - Latif, Zohaib

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Y1 - 2023

N2 - This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2% accuracy.

AB - This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2% accuracy.

KW - Deep Learning

KW - Event Prediction

KW - Sentiment

UR - https://www.scopus.com/pages/publications/85184601073

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DO - 10.1109/ICTC58733.2023.10392730

M3 - Conference contribution

AN - SCOPUS:85184601073

T3 - International Conference on ICT Convergence

SP - 42

EP - 47

BT - ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

T2 - 14th International Conference on Information and Communication Technology Convergence, ICTC 2023

Y2 - 11 October 2023 through 13 October 2023

ER -

Deep Learning-Based Event Prediction for Text Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this