IEEE Computational Intelligence Magazine - February 2020 - 78

involves detecting whether a given text
has a positive, negative, or neutral sentiment at a global level. The BB_twtr [21]
team used an ensemble of Neural Networks combining Convolutional Neural
Networks (CNNs) and Long-Short
Term Memory Networks (LSTMs). The
DataStories [22] system follows a similar
deep learning approach using Bidirectional LSTMs (BiLSTM) with an attention mechanism. Both approaches use
word embeddings from pre-trained vectors as text representation. In the case of
the Arabic language, NileTMRG team
[23] used a Naive Bayes classifier augmented with phrase and word level sentiment lexicon for Egyptian and Modern
Standard Arabic. Two multilingual systems were proposed for this task, SiTAKA
[24] and ELiRF-UPV [25] which participated in English and Arabic. SiTAKA
system uses pre-trained embeddings,
Word2Vec for English, and SKIP-G300
[26] for Arabic. This system also uses
other features such as n-words, part of
speech tags, and lexicons to give an additional score. It uses a Support Vector
Machine (SVM) to perform the classification. ELiRF-UPV system is based on
Convolutional Recurrent Neural Networks (CRNNs) and the combination
of general and specific word embeddings
for English and Arabic, and polarity
information from lexicons.
SemEval 2018 [7] consisted of an array
of subtasks where the systems have to
infer the emotional state of a person based
on his/her tweets. The tasks include the
automatic determination of emotion
intensity (EI) and valence classification
(VC). The former tries to determine the
emotional intensity of tweets; it considers
four basic emotions: anger, fear, joy, and
sadness. The latter,VC, consists on, given a
tweet, classify it into one of seven ordinal
classes related to various levels of positive
and negative sentiment intensity. All tasks
were run for English, Arabic, and Spanish
languages. In this competition, SeerNet
system [27], participating only in English,
proposed a pipeline of pre-processing and
feature extraction steps. The pre-processing uses Tweettokenize3 tool, and for
3

https://github.com/jaredks/tweetokenize

feature extraction, several deep learning
approaches were considered, such as
DeepMoji, EmoInt, Sentiment Neuron,
and Skip-Thought Vectors. EiTAKA [28]
presented results for English and Arabic
using an ensemble of two approaches,
deep learning and XGBoost regressor
based on embeddings and lexicons. As a
multilingual system, AffecThor [29] participated in all the languages and emotional intensity and valence task. The
AffecThor team proposed a solution build
upon several best past-years participating
systems and a combination of several
approaches based on lexical resources and
semantic representations. These resources
include 22 lexicons and Word2Vec for
word embeddings. In the classification
step, they use the architecture of several
neural models like CNN with max pooling, BiLSTM with attention, and a set of
character and word features BiLSTMs
(CHAR-LSTM).
TASS 2017 competition [6] focused
on polarity classification at tweet level
(positive, negative, neutral, and none) in
the Spanish language. The systems were
evaluated on two datasets: the International TASS corpus (InterTASS), tweets
located inside Spain territory written in
the Spanish language; and the General
Corpus, tweets of personalities and
celebrities written in Spanish from several countries including Spain. ELiRFUPV [30] employed different approaches,
i.e., bag-of-words, bag-of-chars, word
embeddings, and one-shot vectors over
words and characters representations, as
well as, Multilayer Perceptron (MPL),
RNNs, CNNs, and LSTM networks.
TASS 2018 edition [5] proposed
tasks including the identification of positive or negative emotions that can
arouse in news, i.e., classify news articles
into SAFE (positive emotions, so safe
for ads) or UNSAFE (negative emotions, so better avoid ads), as a kind of
stance classification according to reader's
point of view. In this task, there were
two subtasks, subtask 1 (S1) consists in
the classification of headlines into either
SAFE or UNSAFE tweets written only
in the Spanish language spoken in Spain;
there were two test sets, named L1 and
L2, having as only difference their cardi-

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2020

nality. Moreover, subtask 2 (S2) consists
in evaluating the systems' ability to generalize. For training, participants were
provided with headlines written only in
the Spanish language spoken in Spain,
and for testing, news articles come from
nine different countries of America
in order to encourage generalization.
ELiRF-UPV [31] team used a deep
neural network, Deep Averaging Networks (DANs), and a set of pre-trained
word embeddings for representing the
news headlines.
The IberEval contest is related to
emotions, mostly in the Spanish language.
In its 2018 edition, IberEval promoted
different tasks such as aggressiveness identification [32] and humor analysis [33].
The aggressiveness identification task
(MEX-A3T) is motivated by cyberbullying, hate speech, harassment, among others. It consists of classifying a text, in
Spanish from Mexico, into either aggressive or non-aggressive. CGP [34] system
used an Attention-based LSTM network,
and word embeddings were used over
the sentence. Attention is applied over the
hidden states to estimate the importance
of each word, and this context vector is
used into another LSTM model to estimate whether a tweet is aggressive or not.
Aragon-Lopez [35] team used both a bag
of terms representation and second-order
attributes (SOA). They use an n-gram
representation combined with a CNN as
the classifier.
The HAHA task [33] (Humor Analysis based on Human Annotation) consisted of classifying tweets in Spanish as
humorous or not. U_O-UPV [36] used
a neural network with attention mechanism, word2vec models, and a set of linguistic features such as stylistic (e.g.,
length, counting of emoticons, hashtags),
structural and content (e.g., animal
vocabulary, sexual and obscene vocabulary), and affective (e.g., positive or negative words, counting of words related
to attitudes). The use of different Neural
Networks has not been restricted to the
aforementioned tasks. There are essential
advances on tasks such as in Sarcasm
which could be considered as a verbal
form of irony that toggles the explicit
sentiment found in a text [37]. Joshi

https://github.com/jaredks/tweetokenize

IEEE Computational Intelligence Magazine - February 2020

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2020