Handling negative mentions on social media channels using deep learning* * This paper is an extended version of our previous work that was presented at ICCSAMA 2017 (Nguyen, Vo, Pham, Nguyen, & Quan, 2017) and had been selected for possible publication in the Journal of Information and Telecommunication published by Taylor & FrancisView all notes

ABSTRACT Social media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions. This kind of task, popular known as sentiment analysis, has been addressed recently by many deep learning approaches. However, negative mentions on social media have their own language characteristics which require certain adaptation and improvement from existing works for better performance. In this paper, we propose a new architecture for handling negative mentions on social media channels. As compared to the architecture published in our previous work, we expose substantial change in the combination manner of deep neural network layers for better training and classification performance on social-oriented messages. We also propose the way to re-train the pre-trained embedded words for better reflect sentiment terms, introducing the resultant sentimentally-embedded word vectors. Finally, we introduce the concept of a penalty matrix which incurs more reasonable loss function when handling negative mentions. Our experiments on real datasets demonstrated significant improvement