Abstract
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.
Highlights
In these years, short text information is continuously being created due to the explosion of online communication, social networks, and e-commerce platforms
We evaluate the use of four word embedding representations based on Word2Vec [9,13] and Bidirectional Encoder Representations from Transformer (BERT) [10] algorithms for the task of toxicity detection in online textual comments
The literature already showed [50] that deep learning methods that are trained with word embeddings outperform those trained with tf-idf features
Summary
Short text information is continuously being created due to the explosion of online communication, social networks, and e-commerce platforms. All of these approaches fall within the Sentiment Analysis research topic, which classifies data into positive or negative classes, and it includes several subtasks, such as emotion detection, aspect-based polarity detection [8], etc To detect such knowledge, supervised Machine Learning-based systems are designed and provided by the research community to support and improve online services to mine and use the information. We analyzed four deep learning models based on Dense, Convolutional Neural Network (CNN), and Long-Short Term Memory (LSTM) layers to detect various levels of toxicity within online textual comments. We evaluate the use of four word embedding representations based on Word2Vec [9,13] and Bidirectional Encoder Representations from Transformer (BERT) [10] algorithms for the task of toxicity detection in online textual comments.
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