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Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models
University of New York Tirana, Albania.
University of New York Tirana, Albania.
Norwegian University of Science and Technology, Norway.
Linnaeus University, Faculty of Technology, Department of Informatics. Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-0199-2377
2022 (English)In: Foundations of Intelligent Systems. ISMIS 2022 / [ed] Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W., Springer, 2022, p. 13-23Conference paper, Published paper (Refereed)
Abstract [en]

Automatic text-based sentiment analysis and emotion detection on social media platforms has gained tremendous popularity recently due to its widespread application reach, despite the unavailability of a massive amount of labeled datasets. With social media platforms in the limelight in recent years, it’s easier for people to express their opinions and reach a larger target audience via Twitter and Facebook. Large tweet postings provide researchers with much data to train deep learning models for analysis and predictions for various applications. However, deep learning-based supervised learning is data-hungry and relies heavily on abundant labeled data, which remains a challenge. To address this issue, we have created a large-scale labeled emotion dataset of 1.83 million tweets by harnessing emotion-indicative emojis available in tweets. We conducted a set of experiments on our distant-supervised labeled dataset using conventional machine learning and deep learning models for estimating sentiment polarity and multi-class emotion detection. Our experimental results revealed that deep neural networks such as BiLSTM and CNN-BiLSTM outperform other models in both sentiment polarity and multi-class emotion classification tasks achieving an F1 score of 62.21% and 39.46%, respectively, an average performance improvement of nearly 2–3 percentage points on the baseline results.

Place, publisher, year, edition, pages
Springer, 2022. p. 13-23
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13515
National Category
Computer Systems
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-116563DOI: 10.1007/978-3-031-16564-1_2Scopus ID: 2-s2.0-85140445228ISBN: 9783031165634 (print)ISBN: 9783031165641 (electronic)OAI: oai:DiVA.org:lnu-116563DiVA, id: diva2:1699599
Conference
26th International Symposium, ISMIS 2022, Cosenza, Italy, October 3–5, 2022
Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2022-11-16Bibliographically approved

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Kastrati, Zenun

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Citation style
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Output format
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