Abstract
The wealth of all human created data is in the form of text and being able to detect emotion in text and classify them provides great insight into how people express themselves on various topics. We consider the emotion classi cation of text by resolving the strength of association of certain parts of speech and n-grams of those parts of speech to an emotion, e.g. how strongly the noun `dark' is associated to the emotions happy, fear, sad, surprise, and anger. Using the method of emotion hash tags and seed terms we collected a corpus from a micro blogging site, twitter. Twitter is an extremely popular communications tool and is a great resource for emotional data. Using our method we are able to identify certain terms which can be related to an emotion and use the presence of these terms as a feature. We built a classi er that can categorise a tweet into one of ve emotion categories. The contribution of this paper is a method which can classify tweets which express emotion and are highly implicit, having no emotion keywords. Previously these tweets may be eliminated from the training sets and test sets or be labeled as neutral, we aim to classify them into an emotion category.