Convolutional Neural Networks for Toxic Comment Classification

Abstract: Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.

• The paper presents CNNs as a viable alternative to bag-of-words methods for toxic comment classification by effectively using word embeddings.
• Using both random and pre-trained embeddings, the study shows that CNNs, particularly the pre-trained variant, achieve over 90% accuracy compared to traditional methods.
• The findings advocate further research into optimizing CNN architectures for enhanced text context modeling to improve automated moderation systems.

Convolutional Neural Networks for Toxic Comment Classification

The paper presents a detailed exploration of employing Convolutional Neural Networks (CNNs) for the task of toxic comment classification—a growing challenge in managing online communications and ensuring a safe online environment. The work sets out to evaluate CNNs' performance compared to traditional bag-of-words (BoW) approaches coupled with established text classification techniques like Support Vector Machines (SVM), Naive Bayes (NB), k-Nearest Neighbors (kNN), and Linear Discriminant Analysis (LDA).

Methodology

The core contribution lies in leveraging CNNs, traditionally effective in computer vision tasks, for text analysis by taking advantage of word embeddings. Two variants are tested: $CNN_{rand}$, where word embeddings are initialized randomly and optimized during training, and $CNN_{fix}$, which uses pre-trained embeddings from the word2vec model. This approach contrasts with BoW models, which rely on document-term matrices and frequently suffer from sparsity issues, limiting their ability to capture the nuances of natural language.

For the BoW baseline, the authors utilize term frequency-inverse document frequency (TF-IDF) to score words based on their importance across documents, forming a dense representation of text data for subsequent classification. Strong emphasis is placed on comparing these methods using metrics such as Recall, Precision, F1-score, Accuracy, Specificity, and False Discovery Rate across multiple iterations.

Results

Empirical results demonstrate that CNNs, particularly $CNN_{fix}$ with pre-trained embeddings, achieve superior accuracy and lower error rates compared to traditional methods, reaffirming the potential of deep learning techniques in text classification. The paper reports $CNN_{fix}$ achieving over 90% accuracy, outpacing the results of other tested methods like SVM and LDA, which hover around 81%. Importantly, CNN-based methods also achieve higher Precision, indicating a reduced rate of false positives—critical in toxic comment classification where false identification can lead to unnecessary content removal and user frustration.

Implications and Future Work

This research situates CNNs as compelling tools for toxic comment detection, particularly in environments with large-scale data where word context and nuanced language understanding are pivotal. The findings suggest an increased relevance for word embeddings over traditional BoW techniques, calling for further research into optimizing CNN architectures and training strategies, potentially through exploring deeper networks, augmented datasets, or hybrid models incorporating CNNs with recurrent layers for sequence understanding.

The results bear significance for the ongoing development of automated content moderation systems. As digital ecosystems grow, the ability to robustly identify toxicity using machine learning models offers promising avenues for maintaining respectful dialogue and protecting users. Future work may explore adaptive learning methodologies or integrated systems balancing CNN's strengths with n-gram techniques for comprehensive language profiling.

In conclusion, this paper underscores CNNs' capabilities in text mining for toxic comment classification, highlighting enhanced performance over classical approaches and setting the stage for continued innovations in automatic language intelligence to mitigate online toxicity.