Social media analytics empowering marketing insight: A framework for analyzing affective experiences from social media content Part of the VALIT (Value Indicator Tools | www.valit.fi) and NEMO (Business Value from Negative Emotions | nemohanke.blogspot.fi) research programs. Funded by Tekes (the Finnish Funding Agency for Innovation | www.tekes.fi). Presented May 26, 2016 at EMAC2016, BI Norwegian Business School, Oslo, Norway.

1. SOCIAL MEDIA ANALYTICS EMPOWERING MARKETING INSIGHT:
A FRAMEWORK FOR ANALYZING AFFECTIVE EXPERIENCES
FROM SOCIAL MEDIA CONTENT
Jari Jussila, Tampere University of Technology, Finland
Mika Boedeker, Tampere University of Applied Sciences, Finland
Harri Jalonen, Turku University of Applied Sciences, Finland
Nina Helander, Tampere University of Technology, Finland
References
Bae, Y. & Lee, H. (2012). SenJment analysis of twiLer audiences: Measuring the posiJve or negaJve influence of popular twiLerers. Journal of the American Society for Informa4on Science and Technology, 63(12),
2521–2535.
Fan, W. & Gordon, M. (2014). The power of social media analyJcs. Communica4ons of ACM 57, 6, 74-81.
Paavola, J., & Jalonen, H. (2015). An Approach to Detect and Analyze the Impact of Biased InformaJon Sources in the Social Media. In Proceedings of the 14th European Conference on Cyber Warfare and Security
2015: ECCWS 2015 (p. 213).
Sein, M, Henfridsson, O, Purao, S, Rossi, M. & Lindgren, R. (2011). AcJon Design Research. MIS Quarterly, 35/1, 37-56.
Seo, M, Feldman Barret, L. & Jin, S. (2008). The Structure of Affect: History, Theory, and ImplicaJons for EmoJon Research in OrganizaJons. In Ashkanasy, N. & Cooper, C. (eds.) Research Companion to Emo4on in
Organiza4ons. Bodmin: E. Elgar, 17-44.
Social media analyFcs
• A cost-effecJve way to gather relevant data.
• SophisJcated tools to process the data.
à More precise and valuable markeJng decisions.
Distressed
HosFle
Sleepy
SaFsfied
Excited
Exuberant
Lazy
Angry Curious
Thrilled
Serene
Calm
Dejected
Defiant
Anxious
Droopy
Gloomy
Tranquil
Content
Elated
Astonished
Unconcerned
Indolent
Bewildered
P+A+D+
Figure 1. PAD-based affecFve families Figure 2. An example of the affecFve experiences
of social media discussions (tweets) of the company 1
Results
• Two new indicators for markeJng analyJcs were developed: number of tweets
represenJng four pleasure-arousal combinaJons (PAD-space) and number of
tweets represenJng eight affecJve families (24 disJnct categories).
• By quanJfying each instance of the observed affecJve families it is possible to
visualize affecJve experiences of social media conversaJons in a higher level of
detail.
• The developed radar chart visualizaJon enables to create social media profiles of
companies’ based on the affecJve experiences of social media conversaJons.
SenFment analysis and data collecFon
In this study, NEMO SenJment & Data analyzer tool was used to collect data from two sohware
companies in Finland. A total of 2128 tweets were collected during the Jme period of 22/05/2015 -
06/10/2015.
Human experts were used to classify the tweets into 24 disJnct affecJve family categories, based on
the introduced affecJve terms. Each instance of the idenJfied affecJve families was then visualized
as a radar chart graph. In the radar chart graph the number of tweets represenJng each affecJve
family is visualized and the according four pleasure-arousal combinaJons shown in brackets.
PAD-based affecFve families
The three-dimensional “PAD-space” is presented in a two-dimensional pie-type graphic so, that each
of the four pleasure-arousal combina4ons (elaJon, serenity, lethargy and tension) are further divided
according to dominance to form eight “affecJve families”.
In this way more than just the superordinate level of posiJve and negaJve affect can be detected.
AddiJonally, to make the framework beLer correspond to the natural way of talking about affecJve
experiences, the affecJve families are characterized by selecJng specific affecJve terms that
represent a larger affecJve family and refer to a whole range of similar kind of terms.
SenFment analysis
• A path to gain understanding of customers’ posiJve or negaJve opinions about the
given subject.
• Inability to capture the deeper contents of the affecJve experience.
The purpose of the study is to present a framework for a deeper analysis of affecFve experiences from social media content.
The objecFve of the framework is to serve as an alpha version of an IT arFfact build iteraFvely following acFon design research (ADR) method.
Discussion
• Using human experts to analyze the affecJve experience of social media
conversaJons is labor intensive and not cost-effecJve for large data sets
• The introduced novel markeJng analyJcs was a first step in building an automated
affecJve experience analysis of social media content.
• The next steps:
- to conJnue the data gathering and labelling process of the affecJons,
- through algorithm development and machine learning to build the arJfact, an
automated affecJon analysis tool, i.e. the “leveraged affecFve experience
analysis tool”.
0
10
20
30
40
50
60
70
80
90
100
Curious (ElaFon)
Elated (ElaFon)
Excuberant (ElaFon)
Serene (Serenity)
Content (Serenity)
SaFsfied (Serenity)
Calm (Serenity) Tranquil (Serenity)
Dejected (Lethargy)
Droopy (Lethargy)
Bewildered (Tension)
Anxious (Tension)
Angry (Tension)
Part of the VALIT (Value Indicator Tools | www.valit.fi) and NEMO (Business Value from NegaJve EmoJons | nemohanke.blogspot.fi) research programs.
Funded by Tekes (the Finnish Funding Agency for InnovaJon | www.tekes.fi).
Presented May 26, 2016 at EMAC2016, BI Norwegian Business School, Oslo, Norway.