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Sentiment Polarity ...
Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models
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- Kastrati, Muhamet (författare)
- University of New York Tirana, Albania
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- Biba, Marenglen (författare)
- University of New York Tirana, Albania
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- Imran, Ali Shariq (författare)
- Norwegian University of Science and Technology, Norway
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- Kastrati, Zenun, 1984- (författare)
- Linnéuniversitetet,Institutionen för informatik (IK),Institutionen för datavetenskap och medieteknik (DM)
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(creator_code:org_t)
- 2022-09-26
- 2022
- Engelska.
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Ingår i: Foundations of Intelligent Systems. ISMIS 2022. - Cham : Springer. - 9783031165634 - 9783031165641 ; , s. 13-23
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Software Technology
- Programvaruteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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