Big Data Analysis in Commercial Social Networks: Analysis of Twitter Reviews for Marketing Decision Making

Content generated by users on commercial social networks about products and brands generates large volumes of data that can be transformed into relevant and useful recommendations for marketing decisions. Every day, consumers post their opinions online on social networks about products they have purchased and used, and companies are increasingly interested in tracking this information in real time for better decision making. The main problem is to extract key information from consumers' textual comments and use it automatically to measure the quality of products or brands. In this work, we propose a hybrid approach to automatically analyze these reviews, assigning a quantitative score to negative and positive user content. The analysis of online consumer sentiment has increased significantly in recent years, being crucial to determine the success of businesses in a wide range of sectors, tourism, hospitality and e-commerce. In the same context, this work proposes a framework for analyzing the sentiment of reviews posted on the Twitter network towards products and brands. The first step is the construction of a dataset by collecting a set of reviews posted online on Twitter, processing and cleaning the textual data for better accuracy, and then developing a hybrid approach for product evaluation and polarities creation using lexicon-based methods and machine learning-based analysis techniques.