1. + Jan Žižka
František Dařena
Department
of
Faculty of
Business
Informatics and
Economics
Mendel Czech
University Republic
in Brno
Mining Textual Significant
Expressions Reflecting Opinions in
Natural Languages

2. +
Introduction
 Many companies collect opinions expressed
by their customers.
 These opinions can hide valuable knowledge.
 Discovering the knowledge by people can be
sometimes a very demanding task because
 the opinion database can be very large,
 the customers can use different languages,
 the people can handle the opinions subjectively,
 sometimes additional resources (like lists of positive
and negative words) might be needed.

3. +
Introduction
 Text
mining can reveal units of the texts
(words, phrases, sentences etc.) that can
represent the meaning/sentiment
 Individual
words usually do not bring
enough information
 More information can provide phrases, but
their extraction, based on linguistic
analysis, requires additional knowledge
that is unique for every language

4. +
Objective
The objective is to find a way how a
computer can reveal phrases that
express a certain opinion, without the
exacting and time consuming linguistic
analysis which is miscellaneous for
different natural languages.

5. +
Data description
 Processed data included reviews of hotel clients
collected from publicly available sources
 The reviews were labeled as positive and negative
 Reviews characteristics:
 more than 5,000,000 reviews
 written in more than 25 natural languages
 written only by real customers, based on a real
experience
 written relatively carefully but still containing errors that
are typical for natural languages

6. +
Review examples
 Positive
 The breakfast and the very clean rooms stood out as the best
features of this hotel.
 Clean and moden, the great loation near station. Friendly
reception!
 The rooms are new. The breakfast is also great. We had a really
nice stay.
 Good location - very quiet and good breakfast.
 Negative
 High price charged for internet access which actual cost now
is extreamly low.
 water in the shower did not flow away
 The room was noisy and the room temperature was higher
than normal.
 The air conditioning wasn't working

7. +
Data preparation
 Data collection, cleaning (removing tags, non-
letter characters), converting to upper-case
 Transforming into the bag-of-words
representation, term frequencies (TF) used as
attribute values
 Removing the words with global frequency < 2
 Stemming, stopwords removing, spell
checking, diacritics removal etc. were not
carried out

8. +
Data characteristics – number of
reviews
1200000
1000000
800000
number of reviews
positive
600000
negative
400000
200000
0
English French Spanish German Italian Czech

9. +
Data characteristics – dictionary
sizes
250000
200000
number of unique words
150000
MinTF=1
MinTF=2
100000
50000
0
English German French Spanish Italian Czech

10. +
Finding significant words
 Thanksto having a large collection of labeled
examples a classifier that separates positive and
negative reviews could be created
 To reveal significant attributes (words) a decision
tree was built using the tree-generating algorithm
c5 based on entropy minimization
 The goal was not to achieve the best classification
accuracy but to find relevant attributes that
contribute to assigning a text to a given class
 The significant words appeared in the nodes of the
decision tree

11. +
Finding the significant words
 The classification accuracy which is proportional to
the relevancy of words was between 89.5 – 92.5%
 Thedecision tree provided a list of about 200–300
words significant for classification from the
sentiment perspective
 These words are used as the basis for extraction of
significant expressions in order to prevent from
considering all possible combinations of words

12. +
Extracting significant expressions
 Extraction of significant expressions starts from
the list of significant words, the reviews are
being searched in the proximity of these words
 Significant-expression extracting algorithm
parameters:
D – the distance from a significant word within
which the search is carried out
 N – the number of words forming the significant
expressions
 M – the minimal number of occurrences of a
specific group of words

13. +
An example
 Searching for significant expressions in a review,
the algorithm parameters: D = 3, N = 3.

14. +
Results
 Lists
of significant expressions extracted from the
original text reviews were obtained.
 The expressions need to be considered by people.

15. +
Significant expressions for English

16. +
Significant expressions for
German

17. +
Significant expressions for Spanish

18. +
Significant expressions for Spanish

19. +
Discussion
 Some of the significant expressions were very similar
 The significant expressions were mostly quite
meaningful and potentially useful for the target
audience
 Some of the expressions were naturally not useful at all
 Itis necessary to find a trade-off between the size of
expressions, the length of the texts where the search is
carried out and the informative value of expressions

20. +
Discussion
 Examples of different distances of words forming the same
significant expression "good location"

21. +
Discussion
 But, the same expression can be formed from words from
more contexts:
“... Breakfast was really good. The location is a
little out of the center ...”
or
“Good service. Convenient location”
or
“It is a quiet location for a good nights sleep”

22. +
Handling large collections
 For
languages with large amount of reviews the
datasets were randomly split into subsets
consisting of 50,000 reviews because of memory
requirements and a decision tree was created for
each such subset
 Each
of the 50,000-sample subsets gave almost the
same list of words
 The relevancies of extracted words were averaged

23. +
Conclusions
 A procedure how to apply computers, machine learning, and
natural language processing areas to automatically find
significant expressions was presented
 From the total number of words (80,000–200,000) only about
200–300 were identified as significant and used as the basis
for expressions extraction
 The simple, unified procedure worked well for many
languages
 Following research focuses on preprocessing phase (e.g.
eliminating meaningless words)
 The procedure might be used during the marketing
research or marketing intelligence, for filtering reviews,
generating lists of key-words etc.

24. Thank you for your attention
Vielen Dank für Ihre Aufmerksamkeit
Gracias por vuestra atención
Merci de votre attention
Grazie per la vostra attenzione
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