Deep Learning Based Topics Detection

Detecting topics from textual data streams is an interesting task in social networks studies. Traditional techniques have certain limitations when processing social network data such as tweets and online conversations, because of the large amount of data and noises. Deep learning appears to be a viable approach for harvesting and extracting valuable knowledge from complex systems. Therefore, we suggest using deep autoencoder model with Koreans-H- algorithm and work with the reconstructed data that contains less noise to detect the eventual topics within it. We evaluate the proposed model on two public datasets of annotated topics. Then, we compare our results to three well known methods. According to the results, our deep learning based method for detecting topics from social networks data outperforms all the three methods, and was able to detect perfectly the right topics in unsupervised way.