Sexual Harassment Detection using Machine Learning and Deep Learning Techniques for Bangla Text

Islam, Mujahidul; Rahman, Maqsudur; Ahmed, Md. Tofael; Islam, A. Z. M. Touhidul; Das, Dipankar; Hoque, Mohammed Moshiul

doi:10.1109/ecce57851.2023.10101522

Cited by 2 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results have shown that SVM has outperformed other algorithms with the best accuracy of 92.3% [18]. www.ijacsa.thesai.org Finally, a research in Bangladesh aims to detect sexual harassments from Bangla texts [19]. The research is motivated by the increasing improper usage of the social media.…”

Section: B Similar Workmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Harassment Toward Women in Twitter During Pandemic Based on Machine Learning

Mustapha,

Sabri,

Bakar

et al. 2024

IJACSA

View full text Add to dashboard Cite

Harassment is an offensive behavior, intimidating and could cause discomfort to the victims. In some cases, the harassments could lead to a traumatic experience to the vulnerable victims. Currently, the harassments towards women in social media have become more daring and are rising. The increasing number of the social media users since the Covid-19 pandemic in 2020 might be one of the factor. Due to the problem, this research aims to assist in detecting the harassment sentiments toward women in Twitter. The sentiment analysis is based on a machine learning approach and Support Vector Machine (SVM) has been chosen due its acceptable performance in sentiment classification. The objective of the research is to explore the capability of SVM in the detection of harassments toward women in Twitter. The research methodology covers the data collection using Tweepy, data preprocessing, data labelling using TextBlob, feature extraction using TF-IDF vectorizer and dataset splitting using the Hold-Out method. The algorithm was evaluated using the Confusion Matrix and the ROC analysis. The algorithm was integrated with the Graphical User Interface (GUI) using Streamlit for ease of use. The implementation of the SVM algorithm in detecting the harassments toward women was successful and reliable as it achieved good performance, with 81% accuracy. The recommendations for the SVM model improvement is to train the dataset of other languages and to collect the Twitter data regularly. The performance of SVM would also be compared with other machine learning algorithms for further validations.

show abstract

Section: B Similar Workmentioning

confidence: 99%

“…harassment from Bangla textIncreasing amount of offensive Bangla text in different social media platforms Deep learning algorithms achieved higher accuracies. However SVM outperform other machine learning algorithms [19]. …”

mentioning

confidence: 96%