Digital Marketing with Social Media: What Twitter Says!

Due to the short and simple way of expression on social media platforms such as Facebook and Twitter, millions of people share daily real-time thoughts and opinions about everything. This shared data generates an increasing availability of unstructured, informal and yet valuable information to data science researchers. Traditional approaches are not the wisest path for collecting and studying consumer behavior because they require a large amount of time and resources and therefore lead to considerable losses for companies. In this paper, we develop a system able to identify and classify sentiment represented in an electronic text from Twitter where users post real-time reactions and opinions called tweets; that are sentences limited to 280 characters about everything to improve the decision-making process for companies. To do so, we used tweepy to access Twitters Streaming API, we combined natural language processing techniques with naive Bayes networks to classify users data, we used GIS (geographical information system) and Matplotlib for data visualization and displaying the results. The purpose of this paper is to propose an efficient approach for predicting accurate sentiment from raw unstructured data in order to extract opinions from the Internet and predict online customers preferences, which could be valuable and crucial for economic and marketing researchers.