Sentiment Analysis for Women in STEM using Twitter and Transfer Learning Models


The science, technology, engineering and math (STEM) sector is integral to the nation’s advancement and economy. However, the STEM workforce is perceived as maledominant, and women are systematically tracked away from it. There has been a rising popularity of the gender disparity problem in STEM across social media platforms. Attitudes relating to women influence the careers women choose to pursue. It is thus timely and important to assess the public’s opinion on this topic. This paper proposes a sentiment analysis classification framework that detects the sentiment of social media tweets in relation to women in STEM. To this end, we extracted more than 250,000 relevant tweets and used various open-language models to uncover insights into the perceptions of women in STEM using various open-language models. The study evaluates the performance of multiple machine learning and deep learning methods. We also study the performance of state-of-the-art transformer based models, including bidirectional encoder representations from transformers (BERT), BERTweet, and TimeLMs (Time Language Models), which achieves 96% accuracy in sentiment detection. Results reveal that people’s attitude in response to women in STEM is generally positive on the Twitter platform. However, we observed a significant correlation between positive sentiment in tweets and dates celebrating women’s achievements (e.g. International Day of Women and Girls in Science, and International Women’s Day). This finding demonstrates the impact of such campaigns on the public’s opinion. Therefore, promoting these events among the public can encourage more females to pursue careers in STEM.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application
Additional Information: Copyright © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2023 IEEE 17th International Conference on Semantic Computing
Event Type: Other
Event Dates: 2023-02-01 - 2023-02-03
Uncontrolled Keywords: Women In STEM,Machine Learning,Deep Learning,Transformers,Twitter,Sentiment Analysis,Natural Language Processing
ISBN: 9781665482639
Last Modified: 13 Jun 2024 07:45
Date Deposited: 13 Jan 2023 11:58
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Related URLs: https://ieeexpl ... all-proceedings (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2023-03-20
Accepted Date: 2022-12-15
Authors: Fouad, Shereen (ORCID Profile 0000-0002-4965-7017)
Alkooheji, Ezzaldin



Version: Accepted Version

License: ["licenses_description_other" not defined]

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