Having Fun?: Personalized Activity-Based Mood Prediction in Social Media

People engage in various activities and hobbies as a part of their work as well as for entertainment. Positivity and negativity attributes of a person’s mood and emotions are affected by the activity that they’re engaged in. In addition to that, time is also a fundamental contextual trigger for emotions as activities have been found to occur at particular time. An interesting question is can we design accurate personalized classifiers that can predict a person’s mood or emotions based on these features extracted from his/her posting in social media? Such a classifier would enable caretakers and health personnel to monitor people going through conditions such as depression as well as identifying people in a timely manner who may be prone to such conditions. This paper explores the design, implementation, and evaluation of such a classifier based on the data collected from Twitter. To do so, crowdworkers were first recruited through Amazon’s Mechanical Turk to label the dataset. A number of potential features are then explored to build a general classifier to automatically predict positivity or negativity of users’ tweets. These features include social engagement, gender, language and linguistic styles, and various psychological features. Then in addition to these features, LIWC is used to extract daily activities of users. Observations show how much activities and temporal nature of posting can be useful behavioral cues to develop a personalized classifier that improves the prediction accuracy of tweets of individual users as positive, negative, and neutral.