Emotion detection in text, also known as affective computing in text refers to the use of natural language processing methods to recognize, interpret and simulate human emotions or affects. These emotions maybe the state of the author or the emotional effect intended by the author. Being able to interpret human emotions, the machine adapts itself better and produces appropriate behavior in response to those emotions. On the other hand, being able to simulate human emotions, the machine improves its communication ability and enriches interactivity between human and machine. Emotions in text may be expressed explicitly with emotional words, such as "happy" and "hate" or implicitly through the contexts. There has not been a method of emotion detection in text that can interpret simultaneously many emotions at once with high accuracy.
This dissertation studies the making of emotion detection system in text. Particularly, it investigates what are emotions and the theories about them, how they are presented and how emotions are different from sentiments or opinions. We then make use of natural language processing tools and machine learning techniques to produce an emotional lexicon and several predicting models. Finally, we evaluate them against state-of-the-art methods to verify the effectiveness of the system.