Social Media Mood Predictor

 

Nowadays many people worldwide use social media like Facebook, Twitter and the like. They post, comment and share information about themselves or topics they are interested in.

In the context of PRECIOUS we investigate: Can this data be used, to tell more about a person’s mood?

For instance in [2] a web-based tool called ‘MoonPhrases’ was created to enable Twitter users to reflect about their mood and well-being. A similar approach was taken in [1], it was investigated to improve the classification of Tweets in either positive, neutral or negative sentiment. Moreover in [3] and [4] messages of Twitter users were interpreted to find out how users talk about depression in Tweets and how the usage of sentiment words of a depressed person differ from a not depressed person.

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Therefore our primary hypothesis is that it is possible to leverage mood/emotions from social media messages. The first step consists in collecting emotionally classified messages to build a classifier. As the range of possible emotions is very wide, it is important to choose predefined emotion classes, to limit the resources needed for data collection and processing of the classifier.  For example the emotion stress is very important in context of cardiovascular diseases, which are in the focus of the PRECIOUS project. Therefor a big focus is on collecting messages classified as stressed or not stressed. These classified messages are used to train a statistical model, which can determine if the writer of the message was stressed. Furthermore as a general indicator for well-being the emotions happy and sad, which correlate well with the pleasure dimension of the circumplex affect model, are in the focus here for emotion recognition.

References

[1] Hagen, M.; Potthast, M.; Büchner, M. & Stein, B.,Hanbury, A.; Kazai, G.; Rauber, A. & Fuhr, N. (Eds.), Twitter Sentiment Detection via Ensemble Classification Using Averaged Confidence Scores, Advances in Information Retrieval, Springer International Publishing, 2015, 9022, 741-754

[2] de Choudhury, M.; Gamon, M.; Hoff, A. & Roseway, A. Osmani, V.; Campbell, A. T. & Lukowicz, P. (Eds.), “Moon Phrases”: A Social Media Faciliated Tool for Emotional Reflection and Wellness, Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th Int. Conference on, 2013

[3] de Choudhury, M.; Gamon, M.; Counts, S. & Horvitz, E., Predicting Depression via Social Media, Seventh International AAAI Conference on Weblogs and Social Media, 2013

[4] Minsu Park, Chiyoung Cha, Meeyoung Cha, and Yoorim, Kweon. Depressive moods of users portrayed in twitter. Telecommunications Review, Jun. 2013.

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