CNN-BASED FEATURES FOR FILTERING OF CRISIS RELATED SOCIAL MEDIA MESSAGES

Abstract

Analysis of the likelihood of attributes like real or false awareness, given a series of message from social media, is a common problem in natural language processing (NLP). This paper presents a reliable method for categorizing emergency of messages in Tweeter. We rely on representation of text features by image patterns instead of using original features extracted from text message. The initial text features were extracted with morphological segmentation and statistical analysis of appearance of keywords in messages by NLP techniques. In order to increase the classification accuracy image patterns-based approach was implemented. The transformation of text features into image allows applying convolution operations for patterns detection. This opens the way to combinations of NLP and image analysis where the powers of both are preserved. Convolutional neural networks were performed with image patterns for the final social media sentence classification. Pros and cons of the method were discussed along with comprehensive report of performance.