As a natural consequence of offering many advantages to their users, social media platforms have become a part of daily lives. Recent studies emphasize the necessity of an automated way of detecting the offensive posts in social media since these ‘toxic’ posts have become pervasive. To this end, a novel toxic post detection approach based on Deep Neural Networks was proposed within this study. Given that several word embedding methods exist, we shed light on which word embedding method produces better results when employed with the five most common types of deep neural networks, namely, , , , , and a combination of and . To this end, the word vectors for the given comments were obtained through four different methods, namely, () , () , () , and () the layer of deep neural networks. Eventually, a total of twenty benchmark models were proposed and both trained and evaluated on a gold standard dataset which consists of tweets. According to the experimental result, the best , , was obtained on the proposed model without employing pre-trained word vectors which outperformed the state-of-the-art works and implies the effective embedding ability of s. Other key findings obtained through the conducted experiments are that the models, that constructed word embeddings through the layers, obtained higher s and converged much faster than the models that utilized pre-trained word vectors.
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
