A Deep Learning Approach for the analysis of feelings on social networks

Abstract

Unstructured textual data produced on the Internet is growing rapidly and the analysis of feelings is becoming a challenge because of the limited contextual information they usually contain. Millions of people share daily real-time thoughts and opinions about everything, which generates an unstructured, informative and yet valuable information to data scientists. Traditional approaches are important to the world of consumer behavior because they require a large amount of time and resources, and lead to considerable losses for companies around the world. Text classification is an essential task for automatic natural language processing (NLP) with many applications, such as information retrieval, web search, ranking and spam filtering. The goal of the NLP is to process the text for analysis and extract information for decision support as a first step in our proposed work to propose an efficient and accurate approach for predicting sentiment from raw unstructured data in order to extract opinions from the Internet and predict online popular discussions using a deep learning approach, which can be valuable and decisive for economic and political researchers to serve the country and emerge from crises . In this work we present an approach for the classification of social media discussions about real-world events like popular mobility in Algeria, and we propose an approach to analyze the feelings of social media messages in Algeria. Applying the different stages of the NLP through the use of deep learning. Keywords: Analyze des sentiments, opinion mining, text mining, social networks, deep learning