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Hate Speech Detection In Italian Text Using Deep Learning Techniques
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Book Synopsis Hate Speech Detection in Italian Text Using Deep Learning Techniques by : Matteo Guida
Download or read book Hate Speech Detection in Italian Text Using Deep Learning Techniques written by Matteo Guida and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by : Vivian Siahaan
Download or read book HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-04 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this project is to develop a comprehensive Hate Speech Detection and Sentiment Analysis system using both Machine Learning and Deep Learning techniques. The project aims to create a robust and accurate system that can automatically identify hate speech in text data and perform sentiment analysis to determine the emotions and opinions expressed in the text. The project is designed to address the growing concern over the spread of hate speech and offensive content online. By implementing an automated detection system, it can help social media platforms, content moderators, and online communities to proactively identify and remove harmful content, fostering a safer and more inclusive online environment. Additionally, sentiment analysis plays a crucial role in understanding public opinions, customer feedback, and social media trends. By accurately predicting sentiment, businesses can make data-driven decisions, improve customer satisfaction, and gain valuable insights into consumer preferences. This project focuses on Hate Speech Detection and Sentiment Analysis using both Machine Learning and Deep Learning techniques. It begins with exploring the dataset, analyzing feature distributions, and predicting sentiment using Machine Learning models like Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and AdaBoost, while optimizing their performance through Grid Search for hyperparameter tuning. Subsequently, Deep Learning LSTM and 1D CNN models are implemented for sentiment analysis to capture long-term dependencies and local patterns in the text data. The project starts with exploring the dataset, understanding its structure, and analyzing the distribution of classes for hate speech and sentiment labels. This initial step allows us to gain insights into the dataset and potential challenges. After exploring the data, the distribution of text features, such as word frequency and sentiment scores, is analyzed to identify any patterns or biases that could impact the model's performance. The dataset is then divided into training, validation, and testing sets to evaluate the models' generalization capabilities. Early stopping techniques are utilized during training to prevent overfitting and enhance model generalization. Performance evaluation involves calculating metrics like accuracy, precision, recall, and F1-score to gauge the models' effectiveness. Confusion matrices and visualizations provide further insights into model predictions and potential areas for improvement. A graphical user interface (GUI) is developed using PyQt to facilitate user interaction with the Hate Speech Detection and Sentiment Analysis system. Before training the Deep Learning models, the text data is tokenized and padded for uniform input sequences. The dataset is split into training and validation sets for model evaluation, and early stopping is used to prevent overfitting during training. The final system combines predictions from both Machine Learning and Deep Learning models to provide robust sentiment analysis results. The PyQt GUI allows users to input text and receive real-time sentiment analysis predictions. The LSTM and 1D CNN models, along with their optimized hyperparameters, are saved and deployed for future sentiment analysis tasks. Users can interact with the GUI, analyze sentiment in different texts, and provide feedback for continuous improvement of the Hate Speech Detection and Sentiment Analysis system.
Book Synopsis Hate and Offensive Speech Detection on Arabic Social Media by : Safa Bakheet Alsafari
Download or read book Hate and Offensive Speech Detection on Arabic Social Media written by Safa Bakheet Alsafari and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are witnessing a proliferation of hate speech on social media targeting individuals for their protected characteristics, including ethnicity, religion, gender, and nationality. Our research focuses on devising effective Arabic hate and offensive speech detection frameworks to address this serious issue. In the first part of the thesis, we aim to improve Arabic hate speech detection systems and present our efforts at building binary and multi-class (3-class and 6-class) hate and offensive speech datasets using four robust extraction strategies that we implement based on the four types of hate: religion, ethnicity, nationality, and gender. Next, we develop several 2-class, 3-class, and 6-class machine and deep learning classification models that we train on different feature spaces using a variety of feature extraction techniques. We also investigate how we can develop single and ensemble machine and deep learning models for hate speech detection and conduct extensive experiments to assess the performance of the various learned models on unseen data. The performance outcome is very encouraging compared to prior hate speech studies carried out on Arabic and English corpora. Furthermore, we examine the word-embedding models' effect on the neural network's performance since they were not adequately examined in the literature. Through 2-class, 3-class, and 6-class classification tasks, we investigate the impact of both word-embedding models and neural network architectures on predictive accuracy. We first train several word-embedding models on a large-scale Arabic text corpus. Next, based on our Arabic hate and offensive speech dataset, we train multiple neural networks for each detection task using the pre-trained word embeddings. This task yields a large number of learned models, which allows conducting an exhaustive comparison. One key for improving hate speech detection performance is to have a textual training corpus that is vast and confidently labeled. Thus, in the second part of this thesis, we explore how we can improve hate speech detection and leverage the abundant social media content based on the recent success of semisupervised learning techniques. In particular, we explore two new research directions: (1) adopting semi-supervised self-learning to create a large-scale hate speech corpus and use it to improve hate speech detection models; and (2) build ensemble-based semi-supervised learning systems based on the machine and deep learning models. We empirically demonstrate the effectiveness of these approaches and show that our semi-supervised approaches improve classification performance over supervised hate speech classification methods.
Book Synopsis Proceedings of the 9th Italian Conference on Computational Linguistics CLiC-it 2023 by : AA.VV.
Download or read book Proceedings of the 9th Italian Conference on Computational Linguistics CLiC-it 2023 written by AA.VV. and published by Accademia University Press. This book was released on 2024-06-26 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ninth edition of the Italian Conference on Computational Linguistics (CLiC-it 2023) was held from 30th November to 2nd December 2023 at Ca’ Foscari University of Venice, in the beautiful venue of the Auditorium Santa Margherita - Emanuele Severino. After the edition of 2020, which was organized in fully virtual mode due to the health emergency related to Covid-19, and CLiC-it 2021, which was held in hybrid mode, with CLiC-it 2023 we are back to a fully in-presence conference. Overall, almost 210 participants registered to the conference, confirming that the community is eager to meet in person and to enjoy both the scientific and social events together with the colleagues.
Book Synopsis Countering online hate speech by : Gagliardone, Iginio
Download or read book Countering online hate speech written by Gagliardone, Iginio and published by UNESCO Publishing. This book was released on 2015-06-17 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: The opportunities afforded by the Internet greatly overshadow the challenges. While not forgetting this, we can nevertheless still address some of the problems that arise. Hate speech online is one such problem. But what exactly is hate speech online, and how can we deal with it effectively? As with freedom of expression, on- or offline, UNESCO defends the position that the free flow of information should always be the norm. Counter-speech is generally preferable to suppression of speech. And any response that limits speech needs to be very carefully weighed to ensure that this remains wholly exceptional, and that legitimate robust debate is not curtailed.
Book Synopsis Advances in Information Retrieval by : Gabriella Pasi
Download or read book Advances in Information Retrieval written by Gabriella Pasi and published by Springer. This book was released on 2018-03-20 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 40th European Conference on IR Research, ECIR 2018, held in Grenoble, France, in March 2018. The 39 full papers and 39 short papers presented together with 6 demos, 5 workshops and 3 tutorials, were carefully reviewed and selected from 303 submissions. Accepted papers cover the state of the art in information retrieval including topics such as: topic modeling, deep learning, evaluation, user behavior, document representation, recommendation systems, retrieval methods, learning and classication, and micro-blogs.
Book Synopsis Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications by : Vinit Kumar Gunjan
Download or read book Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications written by Vinit Kumar Gunjan and published by Springer Nature. This book was released on 2022-01-10 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains original, peer-reviewed research articles from the Second International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, held in March 28-29th 2021 at CMR Institute of Technology, Hyderabad, Telangana India. It covers the latest research trends and developments in areas of machine learning, artificial intelligence, neural networks, cyber-physical systems, cybernetics, with emphasis on applications in smart cities, Internet of Things, practical data science and cognition. The book focuses on the comprehensive tenets of artificial intelligence, machine learning and deep learning to emphasize its use in modelling, identification, optimization, prediction, forecasting and control of future intelligent systems. Submissions were solicited of unpublished material, and present in-depth fundamental research contributions from a methodological/application perspective in understanding artificial intelligence and machine learning approaches and their capabilities in solving a diverse range of problems in industries and its real-world applications.
Book Synopsis Natural Language Processing for Social Media by : Atefeh Farzindar
Download or read book Natural Language Processing for Social Media written by Atefeh Farzindar and published by Morgan & Claypool Publishers. This book was released on 2017-12-15 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
Book Synopsis A Comparative Study of Deep Learning Algorithms for Hate Speech Detection on Twitter by : Raymond Mutanga
Download or read book A Comparative Study of Deep Learning Algorithms for Hate Speech Detection on Twitter written by Raymond Mutanga and published by . This book was released on 2021 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Text and Social Media Analytics for Fake News and Hate Speech Detection by : Hemant Kumar Soni
Download or read book Text and Social Media Analytics for Fake News and Hate Speech Detection written by Hemant Kumar Soni and published by CRC Press. This book was released on 2024-08-21 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting the spread of fake news and hate speech on social media platforms. • Covers various approaches, algorithms, and methodologies for fake news and hate speech detection. • Explains the automatic detection and prevention of fake news and hate speech through paralinguistic clues on social media using artificial intelligence. • Discusses the application of machine learning models to learn linguistic characteristics of hate speech over social media platforms. • Emphasizes the role of multilingual and multimodal processing to detect fake news. • Includes research on different optimization techniques, case studies on the identification, prevention, and social impact of fake news, and GitHub repository links to aid understanding. The text is for professionals and scholars of various disciplines interested in fake news and hate speech detection.
Book Synopsis IJCoL - Italian Journal of Computational Linguistics vol. 10, n. 1 june 2024 by : AA.VV.
Download or read book IJCoL - Italian Journal of Computational Linguistics vol. 10, n. 1 june 2024 written by AA.VV. and published by Accademia University Press. This book was released on 2024-07-30 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adapting BLOOM to a new language: A case study for the Italian Pierpaolo Basile, Lucia Siciliani, Elio Musacchio, Marco Polignano, Giovanni Semeraro U-DepPLLaMA: Universal Dependency Parsing via Auto-regressive Large Language Models Claudiu Daniel Hromei, Danilo Croce, Roberto Basili Investigating Text Difficulty and Prerequisite Relation Identification Chiara Alzetta Italian Linguistic Features for Toxic Language Detection in Social Media Leonardo Grotti Publishing the Dictionary of Medieval Latin in the Czech Lands as Linked Data in the LiLa Knowledge Base Federica Gamba, Marco Carlo Passarotti, Paolo Ruffolo
Book Synopsis Speech & Language Processing by : Dan Jurafsky
Download or read book Speech & Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Text, Speech, and Dialogue by : Petr Sojka
Download or read book Text, Speech, and Dialogue written by Petr Sojka and published by Springer Nature. This book was released on 2022-09-15 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Conference on Text, Speech, and Dialogue, TSD 2022, held in Brno, Czech Republic, in September 2022. The 43 papers presented in this volume were carefully reviewed and selected from 94 submissions. The topical sections "Text", "Speech", and "Dialogue" deal with the following issues: speech recognition; corpora and language resources; speech and spoken language generation; tagging, classification and parsing of text and speech; semantic processing of text and speech; integrating applications of text and speech processing; automatic dialogue systems; multimodal techniques and modelling.
Book Synopsis Social Media and Democracy by : Nathaniel Persily
Download or read book Social Media and Democracy written by Nathaniel Persily and published by Cambridge University Press. This book was released on 2020-09-03 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state-of-the-art account of what we know and do not know about the effects of digital technology on democracy.
Book Synopsis EVALITA Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop by : AA.VV.
Download or read book EVALITA Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop written by AA.VV. and published by Accademia University Press. This book was released on 2024-01-17 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: EVALITA 2023 is an initiative of AILC (Associazione Italiana di Linguistica Computazionale) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA) and the Italian Association for Speech Sciences (AISV). As in the previous editions, EVALITA 2023 is organized along a set of selected tasks, which provide participants with opportunities to discuss and explore both emerging and traditional areas of Natural Language Processing and Speech for Italian. The participation is encouraged for teams working both in academic institutions and industrial organizations.
Book Synopsis Abusive and Hate Speech Tweets Detection with Text Generation by : Abhishek Nalamothu
Download or read book Abusive and Hate Speech Tweets Detection with Text Generation written by Abhishek Nalamothu and published by . This book was released on 2019 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text generator.We conduct domain-specific text generation by combining inverse reinforcement learning (IRL) with domain-specific knowledge. Our approach includes two adversarial nets, a text generator and a Reward Approximator (RA). The objective of the text generator is to generate domain-specific text that is hard to discriminate from the real-world domain-specific text. The objective of the reward approximator is to discriminate the generated domain-specific text from the real-world text. During adversarial training, the generator and the RA play a mini-max game and try to arrive at a win-win state. Ultimately, augmenting diversified and semantically meaningful, generated domain-specific data to the existing dataset improves detection of domain-specific text. In addition to developing the Generative Adversarial Network-based framework, we also present a novel evaluation that uses variants of the BLEU metric to measure the diversity of generated text; uses perplexity and cosine similarity to measure the quality of the generated text. Experimental results show that the proposed framework outperforms a previous baseline (IRL without domain knowledge) on harassment (i.e., Abusive and Hate speech) tweet generation. Additionally, the generated tweets effectively augment the training data for online abusive and hate speech detection (tweet classification) resulting in a 9% accuracy improvement in classification using the augmented training set compared to the existing training set.
Book Synopsis Enterprise Information Systems by : Joaquim Filipe
Download or read book Enterprise Information Systems written by Joaquim Filipe and published by Springer Nature. This book was released on with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: