A term weighting method for identifying emotions from text content

Since the inception of the concept of social networking, communication patterns have shifted drastically with the unmitigated trend in socializing over the Internet, especially when people began connecting via mobile devices. Nowadays people tend to use these modern communication systems to share their emotions with each other. Human emotions play a vital role in human relationships and people share their emotions through facial expressions, gestures, speech and text messages. However, text messaging is the most common and widely accepted method to exchange information among peers through the Internet and mobile networks. In comparison to other methods, identifying emotions from text messages is rather difficult for the recipient. Therefore, the need of automating the emotion recognition from textual content has increased. Utilization of text classification techniques can be considered as the most common approach of identifying emotions from textual content. Prior to applying a text classifier, the textual data should be transformed into a data structure that the classifier understands by conforming to a document representation model and term weighting method. For this research Vector Space Model (VSM) is used as the document representation model. This paper proposes an extension to the Term Frequency - Inverse Document Frequency (TF-IDF) weighting method to increase classification accuracy and explains experiments conducted to discover the best term weighting method in vector space to be used in feature (text term) extraction from Aman´s emotion text corpus. The text classification is done using Oracle´s ODM SVM tool and LibSVM tool.