Supervised Learning for Classification of Emotions Based on Twitter Data
Twitter tweets became a goldmine for data miners. The rationale behind this is that the tweets carry sentiments and emotions. Unlike objective and factual professional publishing, user-generated content is richer in opinions, feelings and emotions. The purpose of the project is to develop a deep learning based framework for recognizing emotions in tweets. It makes use of labelled training datasets in order to have learning process and then classify tweets in order to find emotions. These online expressions can have various practical applications. They have been used to predicted stock market fluctuations, book sales, or movie’s financial success. Given some text, emotion recognition algorithms detect which emotions the writer wanted to express when composing it. An algorithm by name Deep Learning for Emotion Recognition (DLER) is proposed and implemented to achieve this. A prototype application is built to demonstrate proof of the concept. The empirical study revealed that the proposed system is better than the state of the art.