Feature Selection with Hybrid Bio-inspired Approach for Classifying
Multi-idiom Social Media Sentiment Analysis
Luís Marcello Moraes Silva
, Carlos Roberto Valêncio
, Geraldo Francisco Donegá Zafalon
and Angelo Cesar Columbini
Institute of Biosciences, São Paulo State University (Unesp), Humanities and Exact Sciences (Ibilce),
Campus São José do Rio Preto, São Paulo, Brazil
Fluminense Federal University (UFF), Niterói, Rio de Janeiro, Brazil
Keywords: Sentiment Analysis, Feature Selection, Cuckoo Search, Genetic Algorithm, Machine Learning, Social Media.
Abstract: Social media sentiment analysis consists on extracting information from users’ comments. It can assist the
decision-making process of companies, aid public health and security and even identify intentions and
opinions about candidates in elections. However, such data come from an environment with big data
characteristics, which can make traditional and manual analysis impracticable because of the high
dimensionality. The implications on the analysis are high computational cost and low quality of results. Up
to date research focuses on how to analyse feelings of users with machine learning and inspired by nature
methods. To analyse such data effectively, a feature selection through cuckoo search and genetic algorithm is
proposed. Machine learning with lexical analysis has become an attractive alternative to overcome this
challenge. This paper aims to present a hybrid bio-inspired approach to realize feature selection and improve
sentiment classification quality. The scientific contribution is the improvement of a classification model
considering pre-processing of the data with different languages and contexts. The results prove that the
developed method enriches the predictive model. There is an improvement of around 13% in accuracy with a
45% average usage of attributes related to traditional analysis.
Social media plays an important role in daily
activities due to the power of communication it
represents, because it can share information at nearly
any time and location to different groups of people
(Yadav and Vishwakarma, 2020). It is possible to
extract useful knowledge to help financial market,
business intelligence (Ko et al., 2017), human
behavior (Vioulès et al., 2018), fake news detection
(Shu et al., 2017) and political interests (Yue et al.,
2019; Oliveira et al, 2017). Social media can be seen
as a source of comments provided by its users, such
comments represents sentiments and opinions that
can be analysed by different methods to aid many
areas (Yue et al., 2019).
Therefore, Big Data analysis has led researchers
to develop natural language processing and sentiment
analysis tools (Lima et al, 2015). Sentiment analysis
or opinion mining is a research field that studies
sentiments and opinions of a person or group towards
some entity such as products, events, other people and
so on (Yue 2019). This can be made by natural
language processing tools that include statistics,
lexical approaches, machine learning (ML) and
hybrid techniques (Iqbal et al., 2019). Moreover,
many researchers use supervised ML to classify
users’ content and analyse the public opinion of a
topic (Kumar and Jaiswal, 2019, Rasool et al., 2020).
Part of the process of opinion mining deals with
cleaning textual data, extracting specific features
from the document and identifying helpful set of
features for future analysis (Hassonah et al., 2020).
Silva, L., Valêncio, C., Zafalon, G. and Columbini, A.
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis.
DOI: 10.5220/0010972800003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 297-307
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
However, social media are part of the Big Data
universe and this is a challenging area because the
users’ comments represent huge, not structured and
noisy data. Such informal text shows unique features
such as slangs, incorrect words, hashtags, mentions to
other users, links and other aspects that need
appropriated treatment. Plus, there are different
contexts associated to the data origin, like product
reviews, and it must be considered for the analysis
(Kumar and Garg, 2019). Also, the majority of papers
consider English textual data only, so there is a gap in
the state of art to deal with two languages, such as
English and Portuguese (Hemmatian and Sohrabi,
2019; Yadav and Vishkarma, 2020). Not considering
those factors could cause bad prediction performance
due to the ambiguity, noise and imprecision (Kumar
and Jaiswal, 2019). Such scenario is also related with
the volume of the data, specially, with high amount of
features that can be extracted. Features or attributes
are represented by words. Even few samples of text
documents could result in many attributes for the
classifier, which can negatively affect the knowledge
extraction (Uysal, 2016).
The techniques used to perform sentiment
analysis can be divided into ML approach and lexicon
approach (Hassonah et al., 2020). Lexicon based
techniques normally uses collections of dictionaries
that are related with previously defined sentimental
words and expressions to address a sentiment to a
document (Appel et al., 2018). ML based techniques
are split into supervised and unsupervised methods.
The supervised method classifies labelled samples
into the given classes, such as positive, negative or
neutral. Unsupervised techniques group not labelled
samples into a given number of groups. State-of-art
shows that ML based approach presents more
feasibility than other methods, but there are still
obstacles like the number of attributes and the time
needed to train and fit the model (Ahmed and Danti,
2016). To overcome problems such as speed,
accuracy, high complexity and dimensionality of the
predictive model, it is necessary to select a good set
of features from the original set (Hassonah et al.,
2020). Yet, selecting the optimal set is hard for
traditional methods, because for 𝑛 attributes, there
are 2
possible sets to verify (Pandey et al., 2020, Li
et al., 2017).
Literature shows that meta-heuristic, nature
inspired, evolutionary and swarm intelligence
algorithms can be used to deal with this challenge.
These methods are being used to solve real-world
problems due to their capability to analyse high
dimensional space and offer a good trade-off
presenting robust solutions without the necessity of
testing all possible sets of features (Kumar and
Jaiswal, 2020). Many works have dealt with opining
mining in social media using swarm intelligence
meta-heuristic algorithms and manage to achieve
reasonable results (Kumar and Jaiswal, 2019,
Hassonah et al., 2020; Rasool et al., 2020). This paper
presents a hybrid approach to deal with opining
mining considering Portuguese and English textual
data in different contexts. Each context owns
particular words and expressions that could lead to
inaccurate and ambiguity analysis. Plus, studies that
consider context-based sentiment analysis show an
improvement in prediction (Kumar and Garg, 2019,
Souza et al., 2018). The main contributions of this
work are summarized as it follows:
Appling feature selection in social media data
with a hybrid approach for ternary
classification considering two different
languages and several contexts;
Proposal of a meta-heuristic algorithm based
on Cuckoo Search (CS) and Genetic Algorithm
(GA) to perform feature selection regards two
fitness functions;
Comparison of our strategy with no feature
selection, with traditional algorithms and
others nature inspired approaches in order to
verify the benefits of each method using four
1.1 Objectives and Methods
Further, our objective is to expose the development of
a nature inspired algorithm based in swarm
intelligence and evolutionary adaptation. Such
algorithm is made from the combination of CS and
GA strategies that aim to achieve a balanced method
of exploration and exploitation.
The experiments were conducted on four distinct
datasets publicly available. The developed algorithm
called Genetic Cuckoo Search (GCS) is applied to
simplify the model by selecting the best feature set to
enhance the accuracy. Two conventional feature
selection methods were applied and four classifiers
had given the results for comparison, they are Naïve
Bayesian (NB), Maximum Entropy (ME), support
vector machines (SVM) and random forest (RF). This
paper also tested the GCS algorithm with the GA and
CS to analyse the benefits of such method.
1.2 Scope and Limitations
Our scope is to use the GCS to perform sentiment
classification with different idioms and contexts to
expose the improvement in the accuracy and present
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a simpler model. This study’s goal doesn’t intent to
determinate the correctness of the translation, neither
to identify malicious text samples that may occur.
Also, the datasets collected have Big Data features
due to its origin, but the amount of samples was
relatively small to allow executions in feasible time.
Several projects were proposed to enable and enhance
sentiment analysis in this scenario (Yadav and
Vishwakarma, 2020). In general, the works manage
some data cleaning process to allow executions.
Some of them deal with the application and
hybridization of different traditional techniques to
enhance classification criteria only (Zainuddin et al.,
2018; Tripathy et al., 2016), while some works apply
nature inspired algorithms with ML to reduce the
dimension size and improve the quality compared
with other methods. Evaluation criteria in
classification problems are based on accuracy
increase and reduction ratio of the features used.
(Akhtar et al., 2017; Kumar and Jaiswal, 2019;
Hassonah et al., 2020; Rasool et al., 2020).
Many studies conduct sentiment analysis with
different and self-made datasets that are obtained
from social media or online reviews. Some are
transformed to adapt to the necessity of each work.
The authors (Akhtar et al., 2017) presented a study
based on two steps to perform aspect-based sentiment
analysis with Particle Swarm Optimization (PSO) and
ML. First, the extracted features are selected by the
PSO with Conditional Random Field, SVM and ME,
and the best models are loaded in the second step that
uses PSO based ensemble with majority and weighted
voting. Ternary classification was executed in two
similar datasets of online reviews of restaurants and
laptops. Experiments showed an improvement of 3%
and 6% in each dataset, with approximate results of
80% and 75% in the accuracy, respectively.
The work of (Kumar and Jaiswal, 2019) uses two
swarm intelligence algorithms called Grey Wolf and
Moth Flame for doing feature selection over two
benchmark datasets from Twitter. It uses the accuracy
of the classifier as the fitness function. Five classifiers
were used and the results were compared with both
optimized approach and the non-optimized. The
conclusion revealed that around 30% of the features
were redundant, while the increase in accuracy was
approximately 10% with the SVM classifier.
Similarly, in (Kumar et al., 2019), an approach
with the meta-heuristic algorithm Cuckoo Search
(CS) is presented. The benchmark dataset from
Keaggle is used for binary classification. Several
initial parameters of CS were tested to identify the
best scenario. The greater accuracy gain was around
9% with NB classifier and the best result was
achieved with SVM. The average of dimension
reduction was 47%, approximately.
In (Hassonah et al., 2020), the authors developed
a hybrid approach based on filter and wrapper
methods of feature selection along with meta-
heuristics techniques. By realizing a ternary
classification, they combined an initial feature
reducer based on the ReliefF filter with the Multi-
Verse Optimizer using SVM. The data is self-
collected from social media and split through several
contexts. After cleaning the data, the features are
extracted and the ReliefF filter removes less
important features selecting 5% up to 55% of them.
Using the SVM accuracy result as fitness function,
the method salves the best feature set found. Results
were compared with other four classifiers and
analysed with GA and PSO techniques. They
empirically concluded that feature reduction rates
reach up to 96.85%, while accuracy increase ranges
between 1% and 15% in the datasets.
This section describes fundamental concepts for
understanding sentiment analysis and feature
selection to improve the quality of the classification.
3.1 Sentiment Analysis
Also known as opinion mining, sentiment analysis is
a group of techniques which is possible to extract the
sentiment or opinion towards some entity present in
texts in different ways (Yue et al., 2020). After the
pre-processing step, sentiment analysis methods
identify the polarity of a sample by using natural
language processing tools. It involves lexicon or ML
tools, usually classifying a sample into positive,
negative or neutral (Kumar and Garg, 2019).
It is possible to study the text from the perspective
of granularity, considering that each text sample as a
document associated with only one polarity.
Methodologically, documents can be analysed by
lexicon-based and ML (Kumar and Jaiswal, 2019).
Lexicon techniques rely on collections of previously
defined words and expressions associated with a
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis
label. ML are split into supervised and unsupervised
methods, such approach needs labelled data and not
labelled data, respectively, to train a model to identify
unseen documents into classes. (Hassonah et al.,
3.1.1 Multi-language Analysis
The majority of work deals with English data only
and sentiment analysis studies in such language are
advanced (Yadav and Vishwakarma, 2020). Yet,
English-speakers stand for about 25% of the users in
Internet, what means that others languages’ analysis
can be explored, added to the fact that Portuguese is
among the top five idioms in the world (Pereira,
2020). In this scenario, multi-idioms approaches
work with strategies to automatically translate textual
data to enable the use of English tools, which is the
strategy that has brought the best results.
The importance the translation is to maintain the
document polarity, not a perfect conversion (Araújo
et al., 2020). Moreover, (Pereira, 2020) points that
each language have particular knowledge. It is
associated with local slangs, expressions and culture.
The Portuguese idiom has few linguistic resources
that may need attention.
3.1.2 Context-based Analysis
Extracting the polarity of informal texts is a
challenging problem due to the incorrectness and lack
of information that often doesn’t include the context
of the data, since the same work can imply different
polarities (Kumar and Garg, 2019). Developing a
context-based sentiment analysis is a significant task
that includes identifying information about the text
with the assistance of an expert user. It may be able
to verify and correct some domain words and
expressions in order to enable the analysis (Yadav
and Vishwakarma, 2020). The context task is dealt in
the data cleaning step, by identifying stop words,
symbols and synonymous that may cause conflict.
They are removed or replaced by other terms (El
Ansari et al., 2018).
3.2 Pre-Processing
Data extracted from social media represents
unstructured textual data needs treatment. It is
necessary to remove noisy and inconsistent text
aspects to allow a later data mining execution (Rout
et al., 2018). The work (Araújo et al., 2020) showed
that automatic translation is a robust and competitive
way to convert the textual data into English, so
translating is the first step to deal with the data.
The following actions are important to be
executed in the documents and are used by many
papers (Rout et al., 2018; Hassonah et al., 2020):
tokenization; removal of stop words, special
characters, symbols, user mentions and links;
stemming and context application. Such actions not
only improve the quality of the analysis but also help
to decrease the number of features. The context
application consists in a set of stop words, symbols
and synonymous defined by an expert user. All the
terms that were not removed or altered before are
verified and updated. The final step in pre-processing
includes the feature extraction. Such process is made
by converting the clean text into a term-document
matrix. Conversion using the conventional term
frequency–inverse document frequency (TF-IDF)
technique to create the feature matrix is wildly
practiced (Kumar and Jaiswal, 2019; Kumar et al.,
3.3 Bio-inspired Techniques
Feature selection can be applied on the feature matrix
to reduce its attributes. If the matrix has 𝑛 colunms, a
reduction method may select 𝑚𝑛 significant
columns. Usually, a traditional wrapper method tries
to find a set of features to maximize a fitness function.
It may represent the accuracy or other quality
measure of a given set of attributes (Kumar and
Jaiswal, 2019). In this scenario, there are several
stochastic approaches to find a good feature set.
CS is swarm intelligence software used to resolve
continuous optimization problems inspired by the
brood parasitism behaviour of the species (Yang and
Deb, 2009). Therefore it must be discretised. That
means that each feature is treated as 0 or 1,
representing absence and presence of the attribute,
respectively. This method is known for classifying
sentiments of social media data effectively and
outperforms many other meta-heuristic techniques,
while other methods aren’t applied as much (Yadav
and Vishwakarma, 2020).
Initially, a set of eggs or solutions are randomly
created and evaluated. Then, until the stop criterion is
achieved, the following principles are applied:
At a time, each cuckoo places one new eggs or
solution 𝑖 in a arbitrarily selected nest 𝑗;
The nests having top quality eggs will carry over
the upcoming iterations;
The total numbers of host nests are fixed, and
0,1is the probability that a host discovers
an egg placed by cuckoo. If the host recognizes
the cuckoo’s egg, it leaves the nest and
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
constructs another one. In that way, the worsts
solutions are replaced by new.
CS uses the Lévy flights to generate a new
solution. Such method is known for having a good
exploration capability (Yang and Deb, 2009). The
size of the random walk performed by Lévy flights is
given by:
𝐿é𝑣𝑦~𝑢 𝑠
,1𝜆 3
The new solution 𝑥
can be implemented by a
global or local Equations (2)-(3). CS uses a balanced
combination of both (Yang, 2017), as presented:
Where 𝑥
is the canditate to be replaced; 𝑥
the new solution to be obtained; 𝛼1; 𝑠,𝑃
and 𝜖∈
0,1 are real numbers; 𝐻. is a Heaviside function;
and 𝑥
are two good solutions previously
discovered. The operator represents entry wise
multiplications between the dimensional terms
(Yang, 2017).
Despite being a fast converting method, it has
some disvantages. This includes premature
convertion and the possibility of getting stuck in local
optimum (Yadav and Vishwakarma, 2020). The GA
could resolve such limitations with its genetic
operators. It could cause a perturbation in the
solutions population appling selection, crossing-over
and mutation.
Such algorithm basically operates by selecting
good solutions and mixing them to create child
solutions. The main objetive is to select good
solutions that can generate better ones if crossed.
Initially, some individuals are chosen by a criterion,
privileging the best ones. Then the solutions are
mixed, selecting specific parts of each pair and
recombing both into two new solutions. The mutation
is a random modification that changes little parts of a
solution and it must occur rarely for not spoiling a
good solution. GA is known to be costly in
computional terms, so its genetic operators must be
called sporadically (Sharma and Kaur, 2020).
To perform sentiment analysis to classify social
media documents into positive, negative or neutral,
we establish a flowchart to deal with it effectively.
Such overview of our approach is presented in Figure
1. The pipeline of our method is presented in five sub-
tasks: (i) brute data achievement and context
association, (ii) translating and pre-processing data,
(iii) feature extraction with TF-IDF, (iv) feature
selection through GCS and (v) training and testing
four supervised ML methods. The main metrics
considered were accuracy and number of selected
attributes. Execution time was used as a tiebreaker
when quality was equated.
4.1 Data Extraction and Pre-processing
Three public datasets were acquired with the addition
of another one from (Valêncio et al., 2020) work. The
names and contexts of each set of documents are
described as it follows.
- tech companies; Crowdflower
- airline
companies; Kaeggle
- politics and world news;
CLASME tech companies and world news. The first
two datasets contains English data and the last two
Portuguese data. Random portions of each dataset
were used to compose the test bases.
Once the data is set, it passes through the
translation process. This is done with Python
programming language and libraries such as Textblob
and NLTK. According to (Pereira et al., 2020), such
translation is a competitive way to generate reliable
results. Next step includes tokenization and removing
noises from the text. Regular expressions were used
to identify and remove links, user’s mentions and
some grammatical errors. All documents correspond
to a single context, so the cleaning step conducted a
special removal of specific stop words, symbols and
synonymous for each context. For instance, “apple”
and “ice cream” are related to food, but in the context
of tech companies, they associated with a company
and an operational system, respectively.
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis
Figure 1: Overview of the approach.
4.2 Feature Extraction
Next, we perform feature extraction through TF-IDF
technique. It transforms the input data into a matrix,
where each line is a document and each column is an
aspect. If a feature appears in the sample, the matrix
cell is marked as1 and0 otherwise. TF-IDF
allows removing most and less occurring words or
aspects. Words with occurrence in two or less
documents and presented in 70% or more samples
were removed.
4.3 GCS Algorithm
The proposed method was made based in the
evidence of state of art. It is a combination of both CS
and GA methods. An overview of the approach is
presented in Figure 2. The algorithm starts initialling
a population of solutions or nest randomly. Then it
starts the procedure of usual CS by generating a new
nest 𝑖 by Lévy flights and replacing other eventual
bad nest 𝑗. The algorithm proceeds to verify the GA
criterion. CS is a wildly used technique that is
commonly modified and combined with several
algorithms (Sharma and Kaur, 2020). It can
efficiently explore the search space, but it doesn’t
necessarily find the best solution and stops the search.
A way to create a bigger diversity in the solutions is
to use GA with CS, so another strategy of search
could be used to aid exploration and exploitation. Due
to the fact that GA is more costly, it should be used
with less frequency.
Therefore, we set the GA calls to be executed in
10% of the executions. Such value presented the best
results in empirical tests. So, our method continues
executing the CS tasks by replacing the worsts nest
by new random ones and ordering the solutions. If the
GA criterion is achieved, all the population passes
through selection, crossing-over, mutation and
revaluation. Finally, the stop criteria are: maximum
generation reached or best nest stagnated in the last
Figure 2: Flowchart of the algorithm.
50 epochs.
The value of a new point given by equations (2)-
(3) is converted to allow binary operations. It is
adjusted to an integer number 𝑝 0,1023. Each
nest’s features are divided into blocks of size 10. For
instance, considering a nest with ten features, a
solution 𝑥15 means that the binary vector
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
representing the nest is 𝑣 0,0,0,0,0,0,1,1,1,1. The
binary values are converted to integer when needed.
Last positions of the solution are converted in the
appropriated interval. New CS solutions are
discovered through already known good solutions
and global and local search are explored alternately.
Selection through tournament using three nests is
used when GA is called. Each nest has a probability
if being chosen, even bad solutions. The best solution
among the three is set until a new population is
created. The crossover is applied in every pair of
solution with crossover probability 𝑃
, otherwise the
child are identical to its parents. Each block of the
solutions is divided in only one point chosen
randomly. Similarly, nests have a mutation
probability 𝑃
of mutation. When selected, each block
may have modification until 40%, each feature is
changed at random, replacing 0 by 1 or 1 by 0.
Nests are evaluated using the ME classifier by
splitting the samples into a training and a test set. The
evaluation is done through the fitness function, where
the higher the value, the better the subset. A good
solution is a subset of theoretical important features
confirmed by the function. Several works uses only
accuracy to fit the model (Kumar and Jaiswal, 2019).
We apply two fitness functions, 𝐹𝐹
and 𝐹𝐹
verify the impact in the quality of the approach, in
which 𝐴𝑐
is the accuracy and 𝑁𝑎𝑋is the number
of selected features from subset 𝑋. They are shown in
Equations (4)-(5):
The experiments of our work were carried out on a
computer with the following specs: Intel Core i5-
7200U CPU @ 2.50GHz; 8 GB RAM; 480 GB SSD;
Windows 10. The pre-processing step, feature
selection and classification were run using Python.
The data was split in the ratio of 70:30 for training
and testing, respectively. Five initial states of the
samples were tested to avoid over-fitting and the
empirical tests involving stochastic methods were
executed five times to ensure statistical relevance.
Initial experiments were conducted to evaluate the
best parameters of the meta-heuristics algorithms and
they are presented in Table 1. Maximum generation
is the limit of executions and the maximum tolerance
is the maximum number of epochs without
improvements in the best solution.
Table 1: Parameters applied.
Population size (𝑃)
Probability CS (𝑃
Solution scale
Crossover prob. (𝑃
Mutation prob. (𝑃
Maximum generations 1000
Maximum tolerance 50
The data used are textual documents chosen at
random from the descripted public datasets. They are
named dataset A, B, C and D, in which the dataset
possess all contexts and the other contains only one
context. As shown in Table 2, each dataset was built
to be similar, except in dataset D. Such datasets were
employed to analyse the effects of the classification
process according to the context and idiom. The
number of features each dataset presented after the
TF-IDF technique application is: 300, 346, 327 and
453, respectively. Table 3 exposes the mean accuracy
obtained with such features for baseline comparisons
among the four algorithms. It is observed that ME
classifier have the best results.
Table 2: Datasets specifications.
Dataset Context Ori
inal lan
e Number of instances Number of features
A Tech, airline, news English and Portuguese 600 1783
B Tech En
lish 600 1708
C Airline English 600 1711
D News Portu
uese 900 2361
Table 3: Datasets accuracy with all features for several classifiers.
Dataset ME (%) NB (%) SVM (%) RF (%)
A 73.77 60.66 73.33 71.77
B 70.77 58.11 72.44 72.33
C 52.55 49.44 52.55 50.00
D 62.00 60.07 60.44 59.70
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis
The tests implement to analyse the quality in terms of
accuracy and percentage of attributes used. Execution
time must be considered as well, because a good
approach must not be too costly. Our experiment’s
approach is described as follows:
Experiment 1 deals with the comparison of the
method without any feature selection
considering two fitness functions;
Experiment 2 consists in verifying the quality
of traditional methods;
Experiment 3 attempts to present the results
with CS and GA stand alone.
5.1 Fitness Function Comparison
For this test, we applied both fitness functions only in
dataset A to analyse the gains of each method. Our
intention here is to verify which function is better to
find the higher accuracy and if the capability of
reduction is worth.
The results applying 𝐹𝐹
with the baseline results
obtained using the TF-IDF method is presented in
Table 4. ME, SVM and RF have achieved the best
accuracy results with ME being the best. Considering
such fitness function, our algorithm was able to
enhance the accuracy roughly by 10%. The traditional
approaches get around 70% of accuracy. Table 5
shows pretty similar results in terms of accuracy. A
nominal improvement is seen in the ME, SVM and
RF classifiers, with an overall sameness. The biggest
increase is seen in the ME algorithm with a grown of
This implies that both fitness function can provide
nearly the same accuracy, but the reduction with 𝐹𝐹
is better as seen in Table 6. The reduction observed is
around 50% and 62%, respectively. However, the
execution time is almost triplicated, which means a
32 minutes delay. The decision maker may choose the
main criterion to decide between 𝐹𝐹
and 𝐹𝐹
. For
the next tests, we chose 𝐹𝐹
because is faster and
achieves the same accuracy results. Due to fact that
this occurs in all four datasets, we explore the meta-
heuristic performance later.
An overall compiled set of results is displayed in
Table 7. The features selected ratio is below 50% in
all datasets and the average is 45.52% of attributes
used in the final classification. Observing the
accuracy values is possible to see an overall increase
comparing with the baseline values present in Table
3. Around 5% to 8% of improvement is seen in the
SVM classifier, which presents it as a good method
worth exploring. NB and RF presented the minors
enhances, NB even showed a worsening in dataset B,
which was the only bad case. Datasets A and B were
observed to be the most difficult to classify.
Finally, the best results were obtained with ME
algorithm in all cases. Due to this, the ME technique
was selected to be the main approach to analyse the
next experiments. The same behaviour was observed
in the following tests.
Table 4: Accuracy comparison with 𝐹𝐹
Optimized only
with TF-IDF
with GCS
Increase in
ME 73.77 84.31 10.54
NB 60.66 64.20 4.46
SVM 73.33 78.77 5.44
RF 71.77 76.65 4.88
Table 5: Accuracy comparison with 𝐹𝐹
Optimized only
with TF-IDF
with GCS
Increase in
ME 73.77 84.58 10.81
NB 60.66 63.40 2.74
SVM 73.33 79.13 5.80
RF 71.77 77.23 5.46
Table 6: Functions metrics comparison.
Function Feature used
time (s)
accuracy (%)
49.54 1384.51 84.31
37.60 3329.16 84.58
5.2 Traditional Methods Comparison
In this test, two techniques were applied, a filter and a
method. The approaches are: K-Best, and
Recursive Feature Elimination (RFE). The wrapper method
uses the ME classifier. Both algorithms need a pre-
established number of attributes to reach. Based on
previous tests, we conducted the tests with 33%,
Table 7: GCS accuracy and features selected results with several classifiers.
Dataset Features
ME (%) NB (%) SVM (%) RF (%)
A 49.54 84.31 64.20 78.77 76.65
B 43.26 85.04 57.88 77.84 72.66
C 41.29 69.86 55.80 60.57 58.68
D 48.02 74.16 65.24 68.91 66.40
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Table 8: Datasets baseline accuracy with best classifier.
Optimized only
with TF-IDF(%)
Optimized with
-Best (%)
Increase in
accuracy (%)
with RFE (%)
Increase in
accuracy (%)
with GCS (%)
Increase in
accuracy (%)
A 73.77 79.22 5.45 79.11 5.34 84.31 10.54
B 72.44* 77.00* 4.56 78.11 5.67 85.04 12.60
C 52.55 63.33 10.78 62.66 10.11 69.86 17.31
D 62.00 69.33** 7.33 68.22 6.22 74.16 12.16
*Achieved with SVM
** Achieved with NB
Table 9: Features selected with nature inspired methods.
Optimized only
with TF-IDF
with CS
selected (%)
Optimized with
selected (%)
with GCS
selected (%)
A 300 139.53 46.51 151.74 50.58 148.62 49.54
B 346 136.11 39.34 153.86 44.47 149.67 43.26
C 327 124.26 38.00 143.29 43.82 135.01 41.29
D 453 192.43 42.48 225.36 49.75 217.53 48.02
50% and 66% of features selected from the total
attributes of each dataset. The goal is to verify if such
methods can obtain good accuracy results.
The comparing results of baseline, traditional
methods and GCS are exposed in Table 8. The
presented values from K-Best and RFE were the
highest found in any quantity of attributes. In general,
the best accuracy results are associated with the ME
classifier, except for the three marked results. They
are linked to SVM and NB classifiers. Both of the
traditional selectors performed similarly with close
values, but the K-Best achieved best results in three
The improvements vary from 4% to 11%, yet the
results are enhanced, GCS performs better in all
datasets. Despite such improvement, there is still the
limitation of setting the number of features to select.
On the other hand, our algorithm improves the
accuracy ratio by 10% to even 17% inn dataset C.
Even considering different size, multi-idiom and
multi-context datasets, GCS maintained the quality. It
is observed that the developed approach works better
on dataset C and worst in dataset A, which means that
multi-context set implies some difficulty to the
method. Datasets B and D vary on the number of
samples, language and context, but the improvement
is equivalent.
5.3 Stochastic Methods Comparison
Lastly, this experiment intends to analyse the
advantages of our method over CS and GA. We
implemented both of the strategies to execute
independently. The same parameters for each method
were maintained.
was applied and the accuracy
considered were obtained with ME algorithm.
Initially, we aim to study the selected features of each
approach. The results presented in Table 9 reveal that
CS is the method that uses less attributes in all
datasets. With an average of 41.58% of used features,
such method has a good overall reduction ratio
reaching even 38% in dataset C. GCS have the second
better ratio selecting 45.52% of the attributes on
average. It has a better usage of the features
comparing with GA. Such method reaches an average
of 47.15% of drafted attributes. Despite being similar
with GCS, the feature use ratio is slightly worse.
Comparing with full size features of original datasets,
GCS reaches 91% to 92% attributes reduction ratio.
The accuracy of the methods regards the ME
classifier and the baseline related improvements is
shown in Table 10. The CS algorithm presents the
worst values despite the results are better than the
traditional methods. This approach manages to
achieve the best feature selection ratio, but lacks
accuracy. On the other hand, GCS and GA obtained
the best results in this case. Both techniques achieved
the same statistical accuracy values, although GA has
presented better nominal results. The experiments
exposed increases from 10% to 17% on average. The
lowest quality was seen in dataset A, while the
highest is presented in dataset C. This reinforces that
these two algorithms have a similar behaviour in all
datasets. Such case is not observable with CS, which
the lowest improvement occurred in dataset D. CS
appears to have a worst performance on bigger
datasets, while the other two maintain their quality.
In order to demonstrate another important fact, a
final comparison was made through execution time.
So far, GCS and GA have showed close results and
no clear advantages over each other. Yet, execution
time was the main criterion to select
over 𝐹𝐹
it is used to differentiate the bio-inspired methods.
Table 11 illustrated such results. Datasets A, B and C
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis
Table 10: Accuracy with ME classifier in nature inspired methods.
Optimized only
with TF-IDF (%)
with CS (%)
Increase in
accuracy (%)
with GA(%)
Increase in
accuracy (%)
Optimized with
GCS (%)
Increase in
accuracy (%)
A 73.77 76.58 2.81 84.74 10.97 84.31 10.54
B 72.44* 75.74 3.30 85.46 13.02 85.04 12.60
C 52.55 56.66 4.11 70.04 17.49 69.86 17.31
D 62.00 63.08 1.08 74.27 12.27 74.16 12.16
*Achieved with SVM
Table 11: Execution time of nature inspired methods in
seconds (s).
with CS
with GA
with GCS
A 495.06 2289.89 1384.51
B 763.24 3497.47 1682.14
C 517.66 3223.18 2036.84
D 1407.01 8239.48 5061.28
have relatively close time due to their size. The
dataset D needed more time in all cases. CS approach
was the less time consuming algorithm. Due to the
fact that CS is known for premature convergence, this
result is expected. Our technique took the
intermediary time to conclude its execution.
Comparing the datasets A and B, it is notable that
execution time almost doubled regarding GA. In
datasets C and D, we observe a 58% and 62% increase
in runtime over GCS, respectively.
Analysing Tables (9)-(11), it is possible to
conclude that GCS is a balanced method between CS
and GA. It has the intermediary values of feature
selecting ratio, accuracy and execution time.
However, it possesses the same statistical accuracy
results of GA and still takes less time to identify the
same solutions. The decision maker may use other
fitness function to reduce the feature selecting ratio,
but our approach is still relevant to perform sentiment
analysis with competitive quality results.
This research explored the sentiment analysis
scenario considering two idioms and different
contexts, differing from most of the papers. An
overview of the approach is exposed and the tasks of
the pre-processing are described. The bio-inspired is
implemented with CS and GA and the procedural
steps are explained. Experiments were conducted to
evaluate the traditional and mate-heuristics methods
using accuracy with all the features for baseline. The
empirical experiments prove that GCS algorithm can
outperform baseline and traditional feature selection
techniques, as well as other meta-heuristics methods.
This paper presents a competitive reduction on
feature selection ratio. Most of the papers present a
30%, 50% and even 96% average reduction in some
datasets. Our method reached 50%-59% average
reduction ratios depending on the dataset in
comparison with TF-IDF strategy. The reduction ratio
reaches around 92% analysing the full size attribute
set for all datasets. Recent techniques improve the
accuracy by 6% to 10% in general. Our approach
achieved an average increase of 12.75%. Datasets
models containing English data only were enhanced
by 12% and 17%, regards dataset B and C. Multi-
idiom and multi-context set presented a 10% increase
on accuracy, as many papers achieved such values
with English data only and without contexts.
Future works may include analysing other idioms
and a verification of valid samples to verify the
impact on the quality, as well as varying parameters
and adding a filter feature selector before the bio-
inspired stage to reduce the number of attributes.
Also, parallelize the wrapper method is a good way to
study a possible reduction in execution time.
This study was financed in part by the Coordenação
de Aperfeiçoamento de Pessoal de Nível Superior -
Brasil (CAPES) and we thank the authors for their
relevant contributions and the datasets owners who
made them available.
Ahmed, S.; Danti, A. Effective sentimental analysis and
opinion mining of web reviews using rule based
classifiers. In: Computational Intelligence in Data
Mining—Volume 1. Springer, New Delhi, 2016. p.
Akhtar, M. S. et al. Feature selection and ensemble
construction: A two-step method for aspect based
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
sentiment analysis. Knowledge-Based Systems, v. 125,
p. 116-135, 2017.
Appel, O. et al. Successes and challenges in developing a
hybrid approach to sentiment analysis. Applied
Intelligence, v. 48, n. 5, p. 1176-1188, 2018.
comparative study of machine translation for
multilingual sentence-level sentiment analysis.
Information Sciences, v. 512, p. 1078-1102, 2020.
EL Ansari, O.; ZAHIR, J.; MOUSANNIF, H. Context-
based sentiment analysis: a survey. In: International
Conference on Model and Data Engineering. Springer,
Cham, 2018. p. 91-97.
Hassonah, M. A. et al. An efficient hybrid filter and
evolutionary wrapper approach for sentiment analysis
of various topics on Twitter. Knowledge-Based
Systems, v. 192, p. 105353, 2020.
Hemmatian, F.; SOHRABI, M. K. A survey on
classification techniques for opinion mining and
sentiment analysis. Artificial Intelligence Review, v. 52,
n. 3, p. 1495-1545, 2019.
IQBAL, F. et al. A hybrid framework for sentiment analysis
using genetic algorithm based feature reduction. IEEE
Access, v. 7, p. 14637-14652, 2019.
KO, N. et al. Identifying product opportunities using social
media mining: application of topic modeling and
chance discovery theory. IEEE Access, v. 6, p. 1680-
1693, 2017.
Kumar, A. et al. Sentiment analysis using cuckoo search for
optimized feature selection on Kaggle tweets.
International Journal of Information Retrieval
research (IJIRR), v. 9, n. 1, p. 1-15, 2019.
Kumar, A.; GARG, G. Systematic literature review on
context-based sentiment analysis in social multimedia.
Multimedia tools and Applications, v. 79, n. 21, p.
15349-15380, 2020.
Kumar, A.; JAISWAL, A. Swarm intelligence based
optimal feature selection for enhanced predictive
sentiment accuracy on twitter. Multimedia Tools and
Applications, v. 78, n. 20, p. 29529-29553, 2019.
Kumar, A.; JAISWAL, A. Systematic literature review of
sentiment analysis on Twitter using soft computing
techniques. Concurrency and Computation: Practice
and Experience, v. 32, n. 1, p. e5107, 2020.
LI, J. et al. Feature selection: A data perspective. ACM
Computing Surveys (CSUR), v. 50, n. 6, p. 1-45, 2017.
Lima, A. C. E.; DE CASTRO, L. N.; CORCHADO, J. M.
A polarity analysis framework for Twitter messages.
Applied Mathematics and Computation, v. 270, p. 756-
767, 2015.
Oliveira, D. J. S.; BERMEJO, P. H. S.; DOS SANTOS, P.
A. Can social media reveal the preferences of voters? A
comparison between sentiment analysis and traditional
opinion polls. Journal of Information Technology &
Politics, v. 14, n. 1, p. 34-45, 2017.
Pandey, A. C.; RAJPOOT, D. S.; SARASWAT, M. Feature
selection method based on hybrid data transformation
and binary binomial cuckoo search. Journal of Ambient
Intelligence and Humanized Computing, v. 11, n. 2, p.
719-738, 2020.
Pereira, D. A. A survey of sentiment analysis in the
Portuguese language. Artificial Intelligence Review, v.
54, n. 2, p. 1087-1115, 2021.
Rasool, A. et al. GAWA–A Feature Selection Method for
Hybrid Sentiment Classification. IEEE Access, v. 8, p.
191850-191861, 2020.
Rout, J. K. et al. A model for sentiment and emotion
analysis of unstructured social media text. Electronic
Commerce Research, v. 18, n. 1, p. 181-199, 2018.
Sharma, M.; KAUR, P. A Comprehensive Analysis of
Nature-Inspired Meta-Heuristic Techniques for Feature
Selection Problem. Archives of Computational Methods
in Engineering, v. 28, n. 3, 2021.
Shu, K. et al. Fake news detection on social media: A data
mining perspective. ACM SIGKDD explorations
Newsletter, v. 19, n. 1, p. 22-36, 2017.
Souza, E. et al. Swarm optimization clustering methods for
opinion mining. Natural computing, v. 19, n. 3, p. 547-
575, 2020.
Tripathy, A.; AGRAWAL, A.; RATH, S. K. Classification
of sentiment reviews using n-gram machine learning
approach. Expert Systems with Applications, v. 57, p.
117-126, 2016.
Uysal, A. K. An improved global feature selection scheme
for text classification. Expert systems with
Applications, v. 43, p. 82-92, 2016.
Valêncio, C. R. et al. Data warehouse design to support
social media analysis in a big data environment.
Journal of Computer Science, p. 126-136, 2020.
Vioules, M. J. et al. Detection of suicide-related posts in
Twitter data streams. IBM Journal of Research and
Development, v. 62, n. 1, p. 7: 1-7: 12, 2018.
Yadav, A.; VISHWAKARMA, D. K. A comparative study
on bio-inspired algorithms for sentiment analysis.
Cluster Computing, v. 23, n. 4, p. 2969-2989, 2020.
Yang, X.; DEB, S. Cuckoo search via Lévy flights. In: 2009
World congress on nature & biologically inspired
computing (NaBIC). Ieee, 2009. p. 210-214.
Yang, X. (Ed.). Nature-inspired algorithms and applied
Optimization. Springer, 2017.
Yue, L. et al. A survey of sentiment analysis in social
media. Knowledge and Information Systems, v. 60, n. 2,
p. 617-663, 2019.
Zainuddin, N.; SELAMAT, A.; IBRAHIM, R. Hybrid
sentiment classification on twitter aspect-based
sentiment analysis. Applied Intelligence, v. 48, n. 5, p.
1218-1232, 2018.
Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis