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Found 6 papers, 0 papers with code
Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities
In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter.
Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network's Filters
Sexism is very common in social media and makes the boundaries of free speech tighter for female users.
When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP
Sexism is very common in social media and makes the boundaries of freedom tighter for feminist and female users.
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques
While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techniques.
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs
In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity.
Modified SMOTE Using Mutual Information and Different Sorts of Entropies
These four pre-processing methods are combined with 1NN and J48 classifiers and their performance are compared with the previous methods on 11 imbalanced datasets from KEEL repository.
