Presentation done by Prof. Josef Mazanec, during IFITT Doctoral Summer School 2015, in Vienna, Austria.

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J. A. Mazanec, http://raptor.mazanec.com:3000 1
IFITT PhD Seminar 2015
Text Mining Ideas & Examples
J. A. Mazanec
Modul University Vienna
J. A. Mazanec, https://raptor.mazanec.com 2
Contents
Purpose of the introductory presentation
Analyzing the projected images of
destinations
Classifying customer reviews via significant
word items
Extracting topics from positive versus
negative reviews

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Destinations under similarity
competition
 Theoretical underpinnings
 Choosing attributes: affective image
 Connotations
 Survey data vs. Internet sources
 Retrieving co-occurrences
 (dis)similarity – Normalized Google Distance
 Visualizing with …
Dendrograms and
Image maps
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Wayne Chase‘s system of
emotionally positive connotations
Adoration Amazement Admiration Appreciation Affection
Amusement Bliss Amorousness Astonishment Cheer
Comfort Devotion Eagerness Delight Contentment
Fondness Enthusiasm Ecstasy Friendliness Excitement
Elation Gladness Infatuation Exhilaration Enjoyment
Gratitude Kindliness Exuberance Euphoria Hope
Liking Fun Exultation Peacefulness Love
Glee Happiness Lust Hilarity Joy
Relief Passion Merriment Jubilation Satisfaction
Tenderness Mirth Pleasure Serenity Trust
Surprise Pride Thankfulness Warmth Thrill
Rapture Wonder Well-being

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Normalized Google Similarity
Distance (Cilibrasi & Vitanyi 2007)
LSL 
SA 
S
S
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Destination countries with similar
connotative environment

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Destination countries in connotative
Google space
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Exercises
 In-class (group) work
Choose destinations and decide on the attributes
Retrieve from Internet with Google queries
Generate dendrograms and maps
Evaluate results
Comment on relative competitiveness

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Classifying online customer reviews
 Objective: significant word items
 Underlying hypothesis
 Practical usage: identify symptomatic words
as early warning signal
 Analytical method: Penalized Support Vector
Machines
 Demo and exercising
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SVM-Support Vector Machine
(Meyer, 2012)

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Extracting topics from positive &
negative online reviews
 Objective: explore customers‘ use of
language
 Underlying hypothesis
 Practical use: automatic doc annotation;
structure of customer language
 Analytical method: Latent Dirichlet Analysis
 Demo and exercising
LDA basics (Blei, 2012)
topic:= probability distribution over a fixed
vocabulary
distribution over topics
per-document distribution over topics
all documents share the same set of topics
topics, per-document topic distributions,
per-document per-word topic assignments =
hidden structure (that likely generated the
observed documents)
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Latent topics (Blei, 2012)
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Graphical model for LDA (Blei, 2012)
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compute the hidden topic structure ( = posterior distribution = conditional
distribution of the hidden variables given the documents)
β: topic distribution over words θ : proportion for topic k in document d
z: topic assignment for word n in doc d w: word n in document d
α, η

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The generative process and posterior
distribution
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References
Becker, N., Werft, W., Toedt, G., Lichter, P. and Benner, A. (2009).
penalizedSVM: a R-package for feature selection SVM
classification. Bioinformatics 25(13): 1711–1712.
Blei, D. (2012). Probabilistic Topic Models. Communications of the
ACM 55(4): 77-84.
Grün, B. and K. Hornik (2011). topicmodels: An R Package for Fitting
Topic Models. Journal of Statistical Software 10(13): 1-30.
Mazanec, J. A. (2010). Tourism-Receiving Countries in Connotative
Google Space. Journal of Travel Research 49 (Nov): 501-512.
Meyer, D. (2011). Support Vector Machines: The Interface to libsvm
in Package e1071. University of Technology, Vienna.
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