Sentiment Analysis on Social Networks for Defining Innovation Problems in Organizations

Abstract

In recent years, social networks have transformed into dynamic platforms where individuals express personal thoughts, share emotions, and generate content on virtually any topic. To harness the potential of this information, we propose a sentiment analysis system for social networks grounded in the autonomic cycle of data analysis tasks paradigm, aimed at identifying innovation challenges within organizations. This autonomic cycle comprises a series of tasks that systematically collect and manage large volumes of unstructured social media data, facilitating the identification of innovation problems through sentiment analysis. These tasks involve steps such as filtering negative tweets, identifying key terms, and clustering these tweets to analyze centroids for additional insights aligned with the five W-model questions: What, Where, When, Why, and Who, which are essential for problem definition. The final stage centers on defining customer-driven innovation challenges based on the clustered data. The paper concludes with a case study analyzing tweets from a fashion enterprise, in which very promising results are obtained.