A Semantic Approach to Emotion Recognition Using IBM Watson Bluemix tone Analyzer and translator Language
Rusul S. B. Al-Maliky · 2019
Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Personal Assistant Agents (PAAs) can assist users to deal with the task of selecting news items and making decisions. The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. Sentiment analysis and emotion detection that aim to build intelligent systems able to recognize and interpret human emotions. Emotions are considered a very important area because they impact interactions, thinking and behaviors. IBM Watson is one of the most famous company, among others, a lot of services for Natural Language Processing. The IBM Watson Developer cloud provides a library of cognitive services as REST APIs which are available on IBM Bluemix. A machine learning information retrieval tool and building on related work in this area which suggests a powerful correlation between the people, emotions, attitude and cognitive processes which shows more documentation that profiling and predicting user's identity is feasible. We introduce an E-ANRS as a solution to problem of cold-start by using IBM Bluemix server for first time, which provides two services are (language translator and tone analyzer). The experimental results obtained from research are, evaluation of RS which gives three parameters being precision (86%), recall (87%), and F1-score (86%). In another side we have two ways to measure accuracy of emotion for our model by using EEG and Self-Assessment-Manikin techniques. By the use of EEG signals as (attention and meditation), electrical activity of neurons within the brain EEG is used. The results of IBM service Language translation and tone analyzer accuracy for 40 tests are 42%. The main objective of this work is to demonstrate the feasibility of a translation-based approach to emotion recognition in texts.