Entity-based Opinion Mining from Spanish Tweets
Fabián Paniagua-Reyes, José A. Reyes-Ortiz and Maricela Bravo
Department of Systems, Metropolitan Autonomous University, Azcapotzalco, Mexico City, Mexico
Keywords: Opinion Mining, Natural Language Processing, Social Networks.
Abstract: Networking service has grown in the last years and therefore, users generate large amounts of data about
entities, where they can express opinions about them. This paper presents an approach for opinion mining
based on entities, which belong to banks, musicians and automobiles. Our approach uses machine learning
techniques in order to classify Spanish tweets into three categories positives, negatives and neutral. A
Support Vector Machine (SVM) and the bag of word model is used to obtain the corresponding class given
a tweet. Our experimentation shows promising results and they validate that entity-based opinion mining is
achievable.
1 INTRODUCTION
Twitter has become one of the most used social
networking services nowadays. Thousands of users
express their opinion about a named entity, such as a
product, service or interesting person. Such opinions
can be charged with a positive, negative o neutral
polarity. They are communicated within 140
characters, called tweets and they are allowed by the
Twitter social network. With regard to opinion
tweets, millions of text are generated daily that
should be used to make decisions about where to
direct an event or action in order to improve a
service, product or the image of a famous person.
In addition, opinion texts can be very useful for
both public and private organizations, since they
provide fresh data about an entity, i.e. data generated
almost instantly. Therefore, the decisions taken have
a database that considers the instantaneous polarity
produced by users on an entity of interest.
Manually processing of all generated information
about opinions is impossible, costly and time-
consuming. However, it is possible to process data
in order to obtain relevant information thanks to data
mining techniques.
But, the fact of being expressions of opinions in
free text, these can be written in any language. It
causes the complexity of processing to increase. In
addition, considering that there is a lack of linguistic
approaches for opinion mining in the Spanish
language, the problem described above is increased.
We have detected a need to have approaches for text
analysis in Spanish.
Therefore, this paper focuses on providing a
linguistic approach for mining opinions using data
from the social network Twitter, to reduce the lack
of it for the Spanish language. These data are short
text, known as tweets. In this paper, we use a
machine learning approach in order to classify texts.
Support Vector Machine (SVM) as supervised
classification algorithm is used to predict the
corresponding class of each tweet into three classes
positive, negative or neutral. The main aim is to
detect the polarity contained in a text message to a
specific entity, such as automotive brand, bank,
artists/musicians.
2 RELATED WORK
Socher model emphasizes the need for improved
Natural Language Processing (NLP). Authors
implement Latent Dirichlet Allocation (LDA),
autoencoder improved by adding to the objective
function topic information. The system generates a
distribution l-dimensional over topics. Classification
task is done over lexicons, PMI n-grams lexicons
(Pointwise mutual information), negation detection
and elongated words. Such system has improved
results, which are reported in (Ren et al., 2016).
Contextual semantics are emphasized in (Saif et
al., 2016), authors use the SentiCircle which is used
for learning directions of the sentiment as the
horizontal axis represents a neutral sentiment, the
400
Paniagua-Reyes, F., Reyes-Ortiz, J. and Bravo, M.
Entity-based Opinion Mining from Spanish Tweets.
DOI: 10.5220/0006484904000407
In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 400-407
ISBN: 978-989-758-255-4
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

vertical axis separates positive and very positive
above and below horizontal axis negative and very
negative sentiment. SentiCircle tasks include term
indexing, term-content vector generation (a
representation of the term in relation to a previously
given value to the sentiment and a degree of
correlation to the context), contextual features
generation. Also capable of using the negated value
of SentiCircle when finding negations. The system is
used for entity analysis and tweet level sentiment
detection. Results show high F1-measure as 85.45.
Two approaches for polarity analysis for building
a framework are mentioned in (Lima et al., 2015),
even though, authors have implemented a hybrid
architecture as results show improvement. The key
elements of the proposed framework are lexicon-
based along machine learning based for polarity
analysis task, automatic generation of the training
set for machine-learning. Short text classification by
contextual verification, entity detection among other
techniques to reduce false positive detection.
In (Trinh et al., 2016) proposes a system based
on, building a sub-divided emotional dictionary in
nouns, verbs, adjectives and adverbs, and a method
for emotional analysis in English and adaptive with
Vietnamese. Core processes include removing
foreign words, Stopwords, icons, post-tagging phase.
A key step is the emotional analysis evaluation as
whether a text is an emotion or non-emotion based.
Emotional Dictionary is based in SO-CAL since has
demonstrated best results for topic experimentation.
Authors claim very good results, average 90.4% in
precision.
A corpus obtained by indirect emotional query
search with emoticons (a set of characters related to
emotion) such as “:)” or “:(” is the start point in
(Martinez-Cámara et al., 2015). Pre-process steps
include removing new line, opposite emoticons, no
clear sentiment emoticons i.e. “:p”, repeated tweets,
repeated letters and laugh normalization. Different
approaches have experimented with vector modeling
Term Frequency, Term Frequency-Inverse
Document Frequency, Term Occurrences and Binary
Term Frequency. Results demonstrate superior
metric results.
The authors (Ren et al., 2016) improve in results
for state-of-art sentiment classification system at
SemEval 2013 different models are proposed.
Single-prototype and multiple-prototype for word
embedding are used for every model which are
Neural Model, Topic-Enriched Word Embeddings
(TEWE) Topic, Sentiment Word and Sentiment
Information in Word Embeddings (SSWE) and
TEWE combined with SSWE (TSWE). A
convolutional neural network is used for sentiment
classification model. Results demonstrate
improvement in multi-prototypes word embeddings
with SSWE and TSWE, F-measure is 86.10 and
86.83 respectively.
The authors in (Terrana et al., 2014) report good
results without the use of third-party technology. For
a corpus obtained from Twitter Social Network, a
different perspective for obtaining the polarity by the
words contained in a tweet with emoticons, positive
for the text with the following character combination
“:)”, ”:D” or negative with “:(“, “;(“ and their
variations. As a result, the lexicon is capable of
mapping and enriching informal expressions with
slang or grammatical error. The word polarity
calculation is obtained by counting the difference in
positive and negative occurrences over the sum of
positive and negative occurrences. Tweet polarity is
given by the averaging the polarity scores of its
words.
In (Severyn and Moschitti, 2015) propose a
framework for sentiment analysis based on deep
learning scheme using a convolutional neural
network. Authors claim their results are compared
with the top positions in SemEval 2015. The
architecture for this framework starts with a
Sentence Matrix with all words from the tweets
represented by distributional vectors. Followed by a
Convolutional Map which objective is to extract
word patterns found in the training instances. The
system needs to learn the limits to make decisions by
implementing an activation function which can be
logistic or hyperbolic tangent. Then pooling is
needed from the activation function helping to
reduce the representation. Another step is calculating
the probability of distribution over the labels using
Softmax. The final step is sentiment analysis by
feeding the network with additional inputs indicating
the target phrase in a tweet. Best accuracy result
obtained is 84.79.
The presented approach in this work is similar to
other works still not the same as in (Sidorov et al.,
2012) where authors do not propose an entity-based
approach resulting in different results. However, it
can be used to support the presented approach here.
Another independent approach to realize
classification in of different words according to a
lexicon (a group of selected words) that best
represent emotions to help determine the polarity of
the sentiment which has shown good results in this
task. This work can be used to support the presented
approach.
Entity-based Opinion Mining from Spanish Tweets
401

Figure 1: System architecture for entity-centred sentiment analysis.
3 SENTIMENT ANALYSIS
APPROACH
In this section is presented the components of the
framework for entity-based for sentiment analysis
for tweets in Spanish these components are shown in
Figure 1, which include the tasks such as pre-
processing of tweets, feature extraction, lexicon
obtaining, feature weighting by frequency of the
occurrences in the tweets of the lexicon and finally,
the classification algorithms used for the three
topics, such as automobiles, banks and musicians.
3.1 Pre-processing of Text
The corpus must be converted to a common
structure by following the next steps, this work helps
to improve the results in sentiment analysis phase
i.e. removing unwanted characters, accents or
foreign language, information that does not add
value, and punctuation.
The steps for pre-processing are
 Cleaning tweets
 Removing URLs, mentions (@) and entities (#)
 Stemming
 Removing stopwords
 Laugh normalization
3.1.1 Cleaning Tweets
The first step to obtain a lexicon from the texts of
tweets is cleaning the text, a method of separating
phrases into words (tokens) and deleting special
characters that are not defined in Unicode
Transformation List (UTF-8).
3.1.2 Removing URLs, Mentions (@) and
Entities (#)
The following step is to remove URL, links to
websites, mentioning users using “@”, named
entities i.e. #shakira #bmw #volvo #ferrari among
others. The objective is to support the algorithms
work more accurately by avoiding classification task
in such useless text.
3.1.3 Stemming
Improving the text includes, for every word in the
lexicon obtained until this point, a process called
Stemming helps to reduce the words to its “root”,
meaning, it is done by removing suffixes or word
variations giving a common word for all its
variations as a result. For this task to be applied it is
needed the Porter algorithm (Porter, 1980). In Table
1, contains an example, words in Spanish from the
tweets, of this process to obtain the roots using the
SnowballStemmer from NLTK.3.1.4
Table 1: Examples of words stemming process.
Word Stem
gustar gust
europa europ
cuenta cuent
hermoso hermos
ilusion ilusion
presumido presumi
musica music
orgullo orgull
proximo proxim
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3.1.4 Removing Stopwords
This step removes words which do not add meaning
or value to the text (tweet), these words are called
(stopwords), thus they are helpless to the opinion
classification task. This list of words contains
articles, prepositions, non-functional verbs among
others. Other words that are considered
nonfunctional are words with a length of two
characters. Negation words “not”/”no” and
affirmative words “yes”/”sí” which length is two
remain intact in the tweets because they are
functional to define the intended polarity in a tweet.
3.1.5 Laugh Normalization
People tend to express several feelings with the
expression of laugh and they do so by the repetition
of patterns. Even tough, for avoiding redundancy in
the style to express laughter, during pre-processing,
the normalization of laugh is considered a step, with
the objective of helping to classify. By using regular
expressions which are a set of rules that are applied
to get the different possible combinations of the
pattern used for laughter and replace with a common
expression, an example of these is in Table 2 where
the symbol (+) means one or more occurrences.
Table 2: Laugh normalization.
Pattern Phrase Normalized laugh
(ja)+ ja jaja
(je)+ jeje jaja
(jo)+ jojojo jaja
(ji)+ jijijiji jaja
(#)?lol #lol jaja
3.2 Lexicon Learning (Entity
Extraction)
The lexicon or dictionary is built in this phase, after
applying the pre-processing, as non-repeated lexical
units from the tweet corpus. This lexicon is the set of
features to be classified and it is called a bag of
words.
With the task of entity extraction, a lexicon
normalized and reduced is obtained, which will be
used to represent every instance (tweet) by the
weighting. The value of a normalized tweet
representation of the text in number is that every
word has a value depending the importance of it
according to the tweet and the corpus. The
representation in numbers allows the next step in the
process, running the algorithm, to do the tweet
classification in positive, negative or neutral. A total
of 5936 words are obtained from the text (corpus of
tweets) that shapes the lexicon vocabulary.
3.3 Vector Space Model
Representation
There are different approaches to obtain the
importance or weighting of the vocabulary from a
short text. This vocabulary is represented by the
vector space model with the model bags of words
(BoW) (Sebastiani, 2002), which consist of a set of
texts and the vocabulary of terms (entities). Every
tweet is represented as a vector 



,

,

,
where every component 

represents the
importance that produces this feature i, word from
the lexicon, in the tweet j relating the words in the
tweet with all the corpus.
For the weighting of words, meaning, determine
the importance of the term in a tweet, it is used the
term frequency of occurrence for a term in a set of
texts in the domain of an entity (TF-IDF).
The term frequency (TF) consist of the number
of times a term (t) from the vocabulary appears in a
tweet (V), see Equation 1, and the inverse frequency
(IDF) determines when a term is common in the set
of tweets, see Equation 2. This information is used
to calculate the value of TF-IDF using Equation (3).
For every term v
i
in a tweet V the value is
normalized in the equation 4.


,

f

,


(1)


,

log
1||
1
|
∈∶

∈
|
1
(2)




,



,


(3)
A normalization phase is carried out from matrix
obtained by applying the Equation (4).






∑
|

|



(4)
where n means total number of tweets and j
represents each tweet.
3.4 Entity-based Polarity Classification
This work relies on weighting the features based on
the importance of terms in a tweet focused on
entities. Specifically, the weighting of terms
concerning the classification of tweets is centered in
three entities which are brand (automobile), bank,
and artists/musicians.
Polarity classification is based on the vectors
produced by the weighting of terms in relation to
Entity-based Opinion Mining from Spanish Tweets
403

every tweet. The terms are weighted by
implementing TF-IDF algorithm explained in the
previous section and then the entity-based tweets
polarity classification process is executed as
supervised learning by evaluating the features. These
vectors are the feed for this next task in
classification which has been executed with the
SVM algorithm to analyze the behavior and to be
able to decide for each tweet its polarity from the
corpus.
The objective of this phase is to build a
supervised learning classifier of tweets capable of
predicting the polarity from three possible categories
positive, negative or neutral. To make this possible it
is necessary to divide the dataset into two groups,
one for training and the remaining for testing.
As for how it has been mentioned before, the
classifier is centered in entities, meaning, it obtains
just one vocabulary for the set of tweets.
The task of opinion classification based on
entities it is done by the algorithm Support Vector
Machines (SVM) (Chang and Lin, 2011) that builds
hyperplanes in an n-dimensional space from the
training tweets, these hyperplanes are used to predict
the class for new tweets. It is done by “plotting”
each feature from the training data and then realizes
a classification task were in a two dimension space a
straight line divides in to n-number or groups, for
the system presented it will be 3 classes, positive,
neutral, and negative. The SVM algorithm iterates
trying to find the set of features that best represent a
class. Next level of complexity is adding n-
dimension to the model so the best way to classify is
by finding hyperplanes to find the features that
adjust to the representation of every class.
The idea is to evaluate sentiment classification
by the execution of the algorithm SVM, since it was
identified as the best option compared with C4.5, K-
NN and Naïve Bayes in previous investigation work
(Reyes-Ortiz et al., 2017), evaluating the weighting
of the terms (TF-IDF) to find the best solution in
terms of precision. To run the algorithm, the WEKA
framework was used (Garner, 1995) Using the
default configuration of the program (10 cross-fold
validation and 66%f the dataset is used for training
and the rest 34% for testing).
4 DATASET
We use a dataset provided in RepLab (Amigó et al.,
2013) for the specific task called "reputation
polarity", whose purpose is to decide whether the
content of a message (Tweet) in Spanish has positive
or negative implications for the reputation of
entities, such as automotive brand, financial
institution, educational institution or person famous
in music. Such entities are grouped into four topics
musicians, banks, universities and car brands. The
dataset is manually tagged into three labels
mentioned by human experts in linguistic.
We rely on that an entity-based opinion mining
can provide promising clues to determine the entity's
reputation. Thus, we focus on three entities for each
topic in order to obtain a polarity from their tweets,
by analyzing texts from Twitter and to determine
whether it has negative or neutral positive
implications. We have decided that the universities
domain entities should be left out because they are
not balanced with respect to the entities of the other
domains.
From the selected data set, 6965 effective texts
were obtained for all entities, for which it was
possible to obtain their Twitter content and, in
addition, they were manually classified with their
label or category for polarity (Positive, Negative,
Neutral) by human experts as indicated in (Amigó et
al., 2013). This data set represents an excellent
frame of reference for the evaluation of opinion
mining algorithms.
For evaluation, we run the experiments with a
specific configuration, due to our paper focuses on
an entity-based opinion mining. Therefore, we
separate the corresponding tweets to the entity under
evaluation from the final data set. Table 3 shows the
number of effective tweets for each entity to leave
out in the corresponding evaluations.
Table 3: Effective tweets for each entity.
Entity Number of tweets
#Bankia 716
#BBVA 623
#Santander 158
#Ferrari 317
#Volkswagen 193
#Yamaha 150
#Shakira 622
#JustinBieber 274
#JenniferLopez 327
The rest of tweets are used to train our classifier
model in each running experiment. For example, to
evaluate #BBVA entity, we use 6342 tweets for
training model and 623 tweets for testing the
classification task.
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5 ENTITY-BASED
EXPERIMENTATION AND
RESULTS
Experimentation consists on to execute a classifier
algorithm to predict the corresponding class for each
tweet for entities.
Each experiment is carried out by removing the
tweets of the entity to be evaluated and then, the rest
of the tweets are used for as training corpus, from
which the vocabulary is learned. Also, we weighed
the terms of lexicon obtained by the bag of word
model and finally, the SVM classifier is employed to
determine the correct polarity of each tweet to be
tested. Nine entities are evaluated, three for each
topic from our corpus.
An evaluation of all experiments is performed
using the well-known metrics Precision (P), Recall
(R) and F-measure, which have been widely used in
any task of textual classification. These metrics
compare the results of the classifier to be evaluated
with the external confidence values (previously
classified tweets), using the following values a) True
Positive (TP) is the number of correct predictions of
the classifier that correspond to the external
judgment of confidence (previously classified
tweets); True Negative (TN) is the number of correct
predictions from the classifier that does not
correspond to the external judgment of confidence;
False Positive (FP) corresponds to the number of
incorrect classifier predictions that correspond to the
external judgment of confidence; and finally, False
Negative (FN) is the number of incorrect predictions
of the classifier that do not correspond to the
external judgment of confidence.
Under these criteria, Precision (P) is used to
evaluate the algorithms in terms of positive
prediction values, which is defined, in Equation (5).




(
5)
Also, Recall (R) is used to express externally the
correct correspondences rate with the previously
classified tweets with high confidence (Equation 6).



(6)
Finally, F-measure that represents the harmonic
mean between Precision and Recall, which is based
on obtaining a weighted unique value between them
(Equation 7).
  2∗
∗

(7)
All experiments use word weighting using the
TF-IDF and SVM algorithm as the classifier. On the
other hand, we show the results based on entities
because it is the essence of this paper. In addition,
we expose and analyze the results in each entity of
interest. So that, Table 4 shows results of polarity
identification for bank entities in terms of Precision,
Recall and F-measure average with the purpose of
summarizing results.
Table 4: Classification results of bank entities.
Entity Precision Recall F-measure
#Bankia 0.66 0.67 0.67
#BBVA 0.71 0.72 0.71
#Santander 0.57 0.54 0.55
Table 5 presents the summary results for music
entities, specifically, musicians like Shakira, Justin
Bieber and Jennifer Lopez are used for identifying
their reputational polarity from Spanish tweets.
Table 5: Classification results of music entities.
Entity Precision Recall F-measure
#Shakira 0.72 0.74 0.73
#JustinBieber 0.52 0.56 0.54
#JenniferLopez 0.63 0.63 0.62
Three entities of car brands are selected from
corpus. Ferrari, Volkswagen and Yamaha are
monitored and results are shown in Table 6.
Table 6: Classification results of automobile entities.
Entity Precision Recall F-measure
#Ferrari 0.56 0.58 0.56
#Volkswagen 0.60 0.56 0.57
#Yamaha 0.77 0.77 0.76
Based on Tables 4, 5 and 6, we have noticed that
the entities “#BBVA”, “Shakira” and “Yamaha”
have show the best results in their respective topics.
The entity “Yamaha” has shown the best results in
the whole corpus, achieving an approximate 76%
effectiveness of the classifier algorithm to determine
the polarity of its tweets.
6 CONCLUSIONS
This paper has presented an approach for opinion
mining, using the networking service called Twitter
for obtaining data about entities. Our approach uses
a machine learning technique in order to predict
Entity-based Opinion Mining from Spanish Tweets
405

polarity of tweets by analyzing their texts. The main
idea is to determine whether words of a tweet has
negative, positive or neutral implications for the
entity in question.
The main contributions of this papers are a) the
extraction of a lexicon from the corpus, which is
useful to classify texts; b) the entity-based classifier
using lexicon for opinion mining from Spanish
tweets; and c) the experimentation carried out
confirms that entity-based opinion mining can
provide promising clues to determine the entity's
reputation.
In this paper, we focus on Support Vector
Machine algorithm to classify tweets, which
performance the best results for the “Yamaha”
entity, achieving an approximate 76% effectiveness
of the classifier algorithm to determine the polarity
type.
It is important to emphasize that this paper has
made a relevant contribution to the problem of lack
of linguistic approaches in Spanish texts since our
technique uses texts extracted from Twitter in
Spanish. In addition, opinion mining approach is
provided as a tool to make possible the analysis of
reputational polarity for entities in Spanish.
As a future work, experimentation can be
realized with other semantic models of
representation, such as word embedding or
distributional models of semantic representation. In
addition, the evaluated approach for opinion mining
can help to monitor online reputation of interest
entity.
ACKNOWLEDGEMENTS
This work was partly supported by SEP-PRODEP.
The authors would like to thank the Autonomous
Metropolitan University Azcapotzalco and SNI-
CONACyT.
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