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… is the topic of my master’s thesis for HUT . I use “possibly” when speaking of modeling affects from mobile user experience data, because I’ve gotten the feeling these last weeks that the “recognising affects”-part just might be the topic of my PhD Now roughly in the middle of my thesis, I give you a summary of what’s happened.

What field is this exactly?

The fields concerned are affective computing, behaviorism, personality psychology and social psychology.

What do I call “mobile user experience data”?

Tom Guarriello defines in his article “Experiencing Experience” the ultimate experience is a cluster of smaller experiences. Consequently, he continues that measuring the user experience is best attained when considering as many elements in the cluster as possible.
For example, if you would consider a romantic dinner, what makes it a great experience is the cluster: candles, the dim light, the flower, the view, the slow background music, the quietness and sharing it with the other person? Take one of these alone doesn’t make the romantic. Two, maybe a little. The more element the better the experience. This is an analogy to multimodality. The more the merrier. Multimodality should be at the core of accurate affect recognition models, as emotions are intrinsically multifaceted. Having multiple modalities not only brings the accuracy of triangulation, but also by not fusing the behavioral components of moods allows to present the emotional context and maybe help to draw other conclusions if complemented by other contextual information.

How do you recognise emotions?

Recognising the user’s emotions can be done through many modalities, the most popular ones being: heart rate, skin galvanic resistance, speech tones and patterns, facial expressions, body postures and self-report. But baring in mind the current mobile phones and that the goal, being to get as large amount of users as possible with as little trouble as possible, it wouldn’t make sense to collect that kind of data, so I am focusing mainly on behavior data. This will also give a lower accuracy in time, so considering the spectrum of affective phenomena (Fig. 1) with time as main differentiating variable, I will focus on moods rather than emotions. Privacy being a concern, it is essential to compute private data in the mobile phone at low processing and battery cost.

Fig.1: Spectrum of affective phenomena (from Oatly, 2006)

These phenomena can be categorized in:

• episodes of emotions (seconds)
• moods (from hours to weeks)
• emotional disorder (from months to years)
• personality traits (lifetime)

In the context of advertising and communication, moods give a good emotional context for mobile ad targeting and filtering.

How do you recognise personality traits?

There are 2 main theories of personality: Eysenck’s model of personality (P-E-N) and the Big Five personality traits. Both have are based on the concept that there are few (respectively 3 or 5) broad dimensions or factors to describe the personality. Two factors both models have in common is neuroticism (tendency to experience negative emotions) and extraversion (tendency to enjoy positive events, especially social events).

Data I am focusing on to recognise personality traits is mobile phone usage, communication behavior, mobile internet behavior and social networks characteristics.

General mobile phone usage:

• How much people personalise their phone (wallpaper and ringing-tones)
• Time spent playing with the phone

Communication behavior:

• The time spent making calls
• The amount of incoming calls
• Time spent sending and receiving SMS messages
• Preference of voice communication or messaging
• How many people are around the person when he speaks on the phone
• The time laps people take to respond to a missed call or SMS
• Speaks on the phone without headset while driving
• The amount of sent MMS
• How often one checks if he has missed any calls/SMSs

Internet behavior:

• Time spent on mobile Internet
• Time spent for social purpose
• Time spent searching

Social network characteristics:

• Size of social network
• Amount of contacts

Once personality traits are modeled, positive and negative moods could be extracted using relevant theories of personality (Gray, Eysenck or Newman) relating to mood, personality and behavior draw a positive correlation between extraversion and positive mood, and between neuroticism (emotional stability) and negative mood (Gomez, 2000).

Triggers to Negative mood:

• Frustrative nonreward (Gray)
• Punishment (Gray)
• Novelty (inversed) (Gray)
• Displeasure (Eysenck)

Triggers to Positive mood:

• Reward (Gray)
• Nonpunishment (Gray)
• Pleasure (Eysenck)

What could correspond to such stimuli for a mobile user?
= What makes you in a good / bad mood when you use your phone?

E.g.

• Out of credit = displeasure
• Often receiving happy smileys = pleasure, reward
• Calls not returned = frustrative nonreward
• Regularity in patterns (or entropy of life) = novelty