Sentiment Analysis-Based Sexual Harassment Detection Using Machine Learning Techniques

Alawneh, Emad; Al-Fawa’reh, Mohammad; Jafar, Mousa Tayseer; Al-Fayoumi, Mustafa

doi:10.1109/isesd53023.2021.9501725

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…A similar works also study the field of harassment that occurs in social media, which is a study initiated by [17]. The objective is to develop a detection system to detect sexual harassment in text.…”

Section: B Similar Workmentioning

confidence: 99%

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

Mustapha,

Sabri,

Bakar

et al. 2024

IJACSA

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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.

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Section: B Similar Workmentioning

confidence: 99%

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

Mustapha,

Sabri,

Bakar

et al. 2024

IJACSA

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show abstract

“…TP and TN denote the number of correctly recognized positive and negative instances in this confusion matrix. FP and FN, on the other hand, represent the number of misclassified negative and positive cases, respectively [40].…”

Section: Evaluation Metricsmentioning

confidence: 99%

VPN and Non-VPN Network Traffic Classification Using Time-Related Features

Al-Fayoumi¹,

Al-Fawa’reh²,

Nashwan³

2022

Computers, Materials &Amp; Continua

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The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for analysis and anomaly detection approaches based on the network traffic's nature. The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services. Therefore, an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance. In this paper, the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets. Therefore, this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic. The proposed models utilize statistical features (SF), Pearson Correlation (PC), and a Genetic Algorithm (GA), preprocessing the traffic samples into net flow traffic to accomplish the experiment's objectives. The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features. The PC-based method performs well in removing different features of network traffic. With a microsecond perpacket prediction time, the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task, a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach. This is extremely promising for the development of real-time traffic analyzers.

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“…Furthermore, Khatua et al used Twitter data to build a multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and bidirectional LSTM, all of which are similar to the approaches outlined in this study (Bi-LSTM) [13]. Their study examined the many types of sexual violence and the associated hazards.…”

Section: Related Workmentioning

confidence: 99%

“…Social media to collect data for a study on gender violence. On the other hand, a study utilizing 0.7 million tweets and a deep learning system discovered that sexual assaults are more likely to be performed by someone who knows than by someone who does not know [13]. Researchers also used Twitter data to construct a detection tool for sexual harassment and cyberbullying using machine learning and frequency inversion document frequency (TF-IDF) [14].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

Ismail¹,

Yusoff²

2022

IJACSA

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Gender-based violence is a public health issue that needs high concern to eliminate discrimination and violence against women and girls. Several cases are through the offline organization and the respective online platform. However, many victims share their experiences and stories on social media platforms. Twitter is one of the methods for locating and identifying gender-based violence based on its type. This paper proposed a hybrid Long Short-Term Memory (LSTM) and Convolution Neural Network CNN with GloVe to perform multiclassification of gender violence. Intimate partner violence, harassment, rape, femicide, sex trafficking, forced marriage, forced abortion, and online violence against women are e eight gender violence keyword for data extraction from Twitter text data. Next is data cleaning to remove unnecessary information. Normalization converts data into a structure the machine can recognize as model input. The evaluation considers cross-entropy loss parameters, learning rate, an optimizer, and epochs. LSTM+GloVe vector embedding outperforms all other methods. CNN-LSTM+Glove and LSTM-CNN+GloVe achieved 0.98 for test accuracy, 0.95 for precision, 0.94 for recall, and 0.95 for the f1-score. The findings can help the public and relevant agencies differentiate and categorize different types of gender violence through text. With this effort, the government can use as one of the mechanisms that indirectly can support monitoring of the current situation of gender violence.

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Sentiment Analysis-Based Sexual Harassment Detection Using Machine Learning Techniques

Cited by 19 publications

References 19 publications

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

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

VPN and Non-VPN Network Traffic Classification Using Time-Related Features

An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

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