ENTER2017 eTourism Conference presentation

1. Do Local Residents and Visitors
Express the Same Sentiments on
Destinations through Social Media?
M.Jabreel, A.Moreno, A.Huertas
Computer Science and Maths. Dept. + Communication Dept. -
Universitat Rovira i Virgili, Tarragona, Spain
Computer Science Dept., Hodeidah University, Hodeidah, Yemen

2.  Motivation
 Automated analysis of the polarity of
tweets sent by tourists and local citizens
 Case study: 10 top destinations in Europe
 Conclusions
Overview

3.  Motivation
 Automated analysis of the polarity of
tweets sent by tourists and local citizens
 Case study: 10 top destinations in Europe
 Conclusions
Overview

4.  There is a huge volume of opinions about
destinations in social media, both from
citizens and from visitors.
 DMOs are very keen on analyzing this
information, as it is very important on the
design of a proper destination brand.
 This work proposes a new automated
sentiment analysis system that can retrieve
and categorize the opinions of locals and
tourists through social media.
Introduction

5.  Motivation
 Automated analysis of the polarity of
tweets sent by tourists and local citizens
 Case Study: 10 top destinations in Europe
 Conclusions
Overview

6. Methodology
 Selection of the destinations to be
analysed.
 Retrieval and pre-processing of tweets.
 Extraction of a set of features from each
tweet.
 Determination of the polarity of each
tweet.
 Comparative analysis of the sentiment of
the opinions of locals and visitors in the
chosen destinations.

7. Methodology
 Selection of the destinations to be
analysed.
 Retrieval and pre-processing of tweets.
 Extraction of a set of features from each
tweet.
 Determination of the polarity of each
tweet.
 Comparative analysis of the sentiment of
the opinions of locals and visitors in the
chosen destinations.

8. Retrieval of tweets
 twiQuery crawler
 English, radius of
15 kilometers from
the city centre,
specific period of
time.
 Two sets of tweets:
locals (3,000) and
visitors (3,000).
 Info. of home city in
Twitter profile

9. Do tweets actually refer to the
destination?
Manual analysis of 400 random tweets sent by
local people from London.
 292/400 (73%) were related to local aspects
of the destination.
 97/400 (24.2%) were conversational tweets
or personal life comments.
 11/400 (2.8%) explicitly referred to other
locations or global news.

10. Pre-processing of tweets (I)
 Normalization:
URLs and user
mentions are
removed, tweets
are lower cased.

11. Pre-processing of tweets (II)
 Normalization
 Tokenization &
POS-tagging
Each tweet is
tokenized &
POS-tagged
(Ark Tweet).

12. Pre-processing of tweets (III)
 Normalization
 Tokenization &
POS-tagging
 Negation:
The suffix “_NEG” is
added to all the
words that appear in
a negated context.

13. Features Extraction (I-Syntactic)
 Number of
occurrences of
each POS.
 Bi-tagged features
(combination of bi-
grams with their
POS tags).

14. Features Extraction (II-Text)
 n-grams:
sequences of n
(1-4) tokens.
 Negated n-
grams: n-grams
in negated
contexts.

15. Features Extraction (III-Semantic)
 Each word is
mapped to a
predefined semantic
cluster.
 1000 clusters in
Ark Tweet
 4960 n-gram
clusters in
Word2vec

16. Features Extraction (IV-Lexicon)
7 opinion lexicons
 Global polarity
of the tweet.
 Average
polarity of the
+/- terms
 Score of the
last +/- term.
 Max/Min +/-
score.

17. Classification
 SentiRich uses a
Support Vector
Machine to classify
each tweet.
 It was trained using
sets of tweets from
SemEval2013.
 Accuracy 68-72%,
outperforming state-
of-the-art sentiment
analysis systems.

18.  Motivation
 Automated analysis of the polarity of
tweets sent by tourists and local citizens
 Case study: 10 top destinations in Europe
 Conclusions
Overview

19.  Top 25 European destinations in 2014, according to
Tripadvisor.
 Destinations whose DMO had sent more than 3,000
English tweets were selected:
Amsterdam, Athens, Barcelona, Berlin, Budapest,
Dublin, Edinburgh, London, Rimini and Vienna.
 For each destination we retrieved the tweets sent in
English from the city centre in the high tourism season of
2014 and 2015 (June 15th-Sept 1st) and the low tourism
season of 2014 (Oct 15th – Dec 30th).
 3000 local tweets and 3000 visitors’ tweets were
randomly chosen for each destination.
Selection of Destinations

20.  The 6000 tweets of each destination (3000 local +
3000 visitors) were classified as positive, negative
or neutral by SentiRich.
 Results are shown in the form of radial plots,
where each of the concentric circles represents
10% of the tweets.
Results

21. Athens
Dublin
Barcelona
London
Amsterdam
Vienna
Berlin
Budapest
Edinburgh
Rimini
Positive Normal Negative
 Positive views are quite
homogeneous in all the
destinations (51 ~ 57%).
 Exception: Rimini (38%).
 Neutral comments range
from 18% (Edinburgh,
Dublin) to 30% (Athens)
 Except Rimini (over 50%).
 The range of negative
views is even wider, from
only 9% in Rimini to more
than 27% in Edinburgh.
Results - Local Residents’ Tweets

22. Athens
Dublin
Barcelona
London
Amsterdam
Vienna
Berlin
Budapest
Edinburgh
Rimini
Positive Normal Negative
 Difference between
positive and negative
opinions:
 Largest: Athens (54% vs
14%, a 40% difference)
and Amsterdam (39%).
 Smallest differences:
Edinburgh (26%), Rimini
and Vienna (29%).
Results - Local Residents’ Tweets

23. Athens
Dublin
Barcelona
London
Amsterdam
Vienna
Berlin
Budapest
Edinburgh
Rimini
Positive Normal Negative
 All the destinations show
between 50 and 56% of
positive opinions.
 23~29% of the comments
by tourists are neutral
 Except Edinburgh (21%) and Rimini
(32%).
 17 (Rimini, Athens) ~ 24%
(Edinburgh) negative
tweets.
Results – Visitors’ Tweets

24. Athens
Dublin
Barcelona
London
Amsterdam
Vienna
Berlin
Budapest
Edinburgh
Rimini
Positive Normal Negative
 Difference between
positive and negative
opinions:
 From 29% (Edinburgh,
Amsterdam) to 36-37%
(Athens, London).
Results – Visitors’ Tweets

25.  Locals mostly make positive comments about their
city in social media, but there is a fair share of
neutral and negative comments.
 There are significant differences among the opinions
of local residents in different cities (Positive >>>
Negative in Athens/Amsterdam, highly Neutral in
Rimini, Negative > Neutral in Dublin/Edinburgh).
 The percentages of positivity, negativity and
neutrality of visitors’ tweets in the different
destinations are much more homogeneous than
those of the local residents.
Comparative analysis

26.  Motivation
 Automated analysis of the polarity of
tweets sent by tourists and local citizens
 Case study: 10 top destinations in Europe
 Conclusions
Overview

27.  It is feasible to build a sentiment analysis
system that can analyze large amounts of
short messages on social media about a
given destination in real time.
 It is possible to compare the opinions on
different destinations or the views of locals
and tourists.
Conclusions

28. Future work
 Remove tweets that do not refer to the
destination.
 Make a more fine-grained analysis of
positive/negative/neutral opinions on
different attributes of the destination.
 Develop a web-based tool that can make
this analysis automatically in real time.

29. Do Local Residents and Visitors
Express the Same Sentiments on
Destinations through Social Media?
M.Jabreel, A.Moreno, A.Huertas
ITAKA-Int.Tech. for Advanced Knowledge Acquisition
Computer Sc. and Maths. Dept. + Communication Dept.
- Universitat Rovira i Virgili, Tarragona
Computer Science Dept., Hodeidah University, Yemen