Understand and contextualize the sentiment of a piece of text just like a human. Using an ensemble of hybrid deep-learning models, comprising of transformer-based architectures, overplayed with sequential and feed-forward neural networks the contextual sentiment knowledge function can classify sentiment with 96% accuracy. Near-human like preciseness is achieved using a new class of models that are superior to old methods reliant upon positive/negative word counts. This knowledge function can understand and interpret the relevant context, with respect to the domain of the text using language understanding and can contextually categorize text like a human.
This is available in 2 languages: English, and Simplified Chinese
The function provides a probability-measure for the sentiment to account for how-positive / negative the text is as a continuous stream, rather than as a bag of words approach.
Humans, in general, are good at qualifying / classifying statements to different classes but not necessarily at quantifying the magnitude. This function, following this principle, is trained as a classifier, and hence achieves much better performance than its competitors
The hybrid ensemble approach is utilized to ensure the performance is maximized with respect to Bayes Human Error. As with every other function on our platform, this function also has the ability to continually learn from new data while mitigating catastrophic forgetting.