Using Word Embeddings and Deep Learning for Supervised Topic Detection in Social Networks

In this paper we show how word embeddings can be used to evaluate semantically the topic detection process in social networks. We propose to create and train a word embeddings with word2vec model to be used for text classification process. Then when the documents are classified, we use a pre-trained word embeddings and two similarity measures for semantic evaluation of the classification process. In particular, we perform experiments with two datasets of Twitter, using both bag-of-words with conventional classification algorithms and word embeddings with deep learning-based classification algorithms. Finally, we perform a benchmark and make some inferences about results.