Search results for "author profiling"
showing 5 items of 5 documents
T100: A modern classic ensemble to profile irony and stereotype spreaders
2022
In this work we propose a novel ensemble model based on deep learning and non-deep learning classifiers. The proposed model was developed by our team for participating at the Profiling Irony and Stereotype Spreaders (ISSs) task hosted at PAN@CLEF2022. Our ensemble (named T100), include a Logistic Regressor (LR) that classifies an author as ISS or not (nISS) considering the predictions provided by a first stage of classifiers. All these classifiers are able to reach state-of-the-art results on several text classification tasks. These classifiers (namely, the voters) are a Convolutional Neural Network (CNN), a Support Vector Machine (SVM), a Decision Tree (DT) and a Naive Bayes (NB) classifie…
An SVM Ensamble Approach to Detect Irony and Stereotype Spreaders on Twitter
2022
The problem we address in this work is classifying whether a Twitter user has spread Irony and Stereotype or not. We used a text vectorization layer to generate Bag-Of-Words sequences. Then such sequences are passed to three different text classifiers (Decision Tree, Convolutional Neural Network, Naive Bayes). Our final classifier is an SVM. To test and validate our approach we used the dataset provided for the author profiling task organized by PAN@CLEF 2022. Our team (missino) submitted the predictions on the provided test set to participate at the shared task. Over several cross fold validation our approach was able to reach a maximum binary accuracy on the best validation split equal to…
Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural Network
2022
In this paper we describe a deep learning model based on a Data Augmentation (DA) layer followed by a Convolutional Neural Network (CNN). The proposed model was developed by our team for the Profiling Irony and Stereotype Spreaders (ISSs) task proposed by the PAN 2022 organizers. As a first step, to classify an author as ISS or not (nISS), we developed a DA layer that expands each sample in the dataset provided. Using this augmented dataset we trained the CNN. Then, to submit our predictions, we apply our DA layer on the samples within the unlabeled test set too. Finally we fed our trained CNN with the augmented test set to generate our final predictions. To develop and test our model we us…
Fake News Spreaders Detection: Sometimes Attention Is Not All You Need
2022
Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN,…
Detection of Hate Speech Spreaders using Convolutional Neural Networks
2021
In this paper we describe a deep learning model based on a Convolutional Neural Network (CNN). The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. Our approach to the task of classifying an author as HSS or not (nHSS) takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the trained model presente…