Towards a Performant Multilingual Model Based on Ensemble Learning to Enhance Sentiment Analysis

The aim of sentiment analysis, known as opinion mining, is to discover subjective information by understanding the meaning over public opinions, standpoints and attitudes from the shared text, such as consumers feedback, which focuses on automated tools. In this paper, we introduce a novel model based on a learning approach to enhance the rate of understanding and predicting the sentiment of a text. Our proposed learning approach establishes an effective penalty mechanism to map out the links between the analyzed context in which the sentiment is similar. With a gold standard corpus we released, the results obtained are better in terms of precision, recall, and computation time cost using a multithreading model.