Emotion Artificial Intelligence Derived from Ensemble Learning
Anasse Bari, Goktug Saatcioglu · 2018
We present in this work a predictive analytics framework that can computationally identify and categorize opinions expressed in text to discover and analyze attitudes towards a particular topic or product. We provide a new approach based on an ensemble model of three widely used sentiment analysis algorithms: TextBlob, OpinionFinder and Stanford NLP. In this work we investigated the performance of these latter algorithms on large, real datasets. Then, we designed two ensembles (1) one based on multivariate regression that computes a final prediction from three classification algorithms and (2) an ensemble that is based on majority rule. We computed the accuracy of the ensemble framework on labeled real datasets used in the literature that include tweets, as well as Amazon, Yelp and IMDb movie reviews. Our experiments indicated that the ensemble algorithms outperformed all three sentiment algorithms. The ensemble learning algorithm draws from the strengths of the individual sentiment algorithms, avoiding the need to select just one algorithm, creating a stronger tool for harnessing Emotion AI. This approach creates promises beyond the tweets and reviews analyzed here and can potentially be applied to marketing, finance, politics, and beyond.