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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence
Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130999
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.
Abstract: 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 multi-classification 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.
Keywords: Gender-based violence; deep learning; convolution neural network; long short-term memory; convolution neural network - long short-term memory; long short-term memory - convolution neural network; global vector; multi-class text classification
Abdul Azim Ismail and Marina Yusoff, “An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130999
@article{Ismail2022,
title = {An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130999},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130999},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Abdul Azim Ismail and Marina Yusoff}
}
