Toxic comment classification using convolutional and recurrent neural networks

This thesis aims to provide a reasonable solution for categorizing automatically sentences into types of toxicity using different types of neural networks. There are six types of categories: Toxic, severe toxic, obscene, threat, insult and identity hate. Three different implementations have been studied to accomplish the objective: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and convolutional neural networks. The thesis is not thought to aim on improving the performance of every individual model but on the comparison between them in terms of natural language processing adequacy. In addition, one differential aspect about this project is the research of LSTM neurons activations and thus the relationship of the words with the final sentence classificatory decision. In conclusion, the three models performed almost equally and the extraction of LSTM activations provided a very accurate and visual understanding of the decisions taken by the network.Esta tesis tiene como objetivo aportar una buena solución para la categorización automática de comentarios abusivos haciendo uso de distintos tipos de redes neuronales. Hay seis categorías: Tóxico, muy tóxico, obsceno, insulto, amenaza y racismo. Se ha hecho una investigación de tres implementaciones para llevar a cabo el objetivo: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) y redes convolucionales. El objetivo de este trabajo no es intentar mejorar al máximo el resultado de la clasificación sino hacer una comparación de los 3 modelos para los mismos parámetros e intentar saber cuál funciona mejor para este caso de procesado de lenguaje. Además, un aspecto diferencial de este proyecto es la investigación sobre las activaciones de las neuronas en el modelo LSTM y su relación con la importancia de las palabras respecto a la clasificación final de la frase. En conclusión, los tres modelos han funcionado de forma casi idéntica y la extracción de las activaciones han proporcionado un conocimiento muy preciso y visual de las decisiones tomadas por la red.Aquesta tesi té com a objectiu aportar una bona solució per categoritzar automàticament comentaris abusius usant diferents tipus de xarxes neuronals. Hi ha sis tipus de categories: Tòxic, molt tòxic, obscè, insult, amenaça i racisme. S'ha fet una recerca de tres implementacions per dur a terme l'objectiu: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) i xarxes convolucionals. L'objectiu d'aquest treball no és intentar millorar al màxim els resultats de classificació sinó fer una comparació dels 3 models pels mateixos paràmetres per tal d'esbrinar quin funciona millor en aquest cas de processat de llenguatge. A més, un aspecte diferencial d'aquest projecte és la recerca sobre les activacions de les neurones al model LSTM i la seva relació amb la importància de les paraules respecte la classificació final de la frase. En conclusió, els tres models han funcionat gairebé idènticament i l'extracció de les activacions van proporcionar un enteniment molt acurat i visual de les decisions preses per la xarxa

Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation

Toxic Comment Classification using Deep Learning

Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes