Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media

Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter's sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.