Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media
Abstract
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.
Description
Keywords
Emerging Topic Detection, interpreting sentiment variations, opinion reason mining,
Sentiment Analysis, Sentiment Reasoning, Sentiment Spikes, Topic Model, Artificial Intelligence, Machine
Learning, Filtered-LDA, FB-LDA.
Citation
Alattar, F. and Shaalan, K. (2021) “Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media,” IEEE Access, 9.