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.

1 INTRODUCTION

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).

https://orcid.org/0000-0002-7201-1257

https://orcid.org/0000-0002-9325-3159

https://orcid.org/0000-0003-2384-011X

https://orcid.org/0000-0002-8906-4128

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

297

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

classifiers.

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.

2 RELATED WORK

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.

3 BACKGROUND

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

299

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.,

2020).

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.,

2019).

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

𝑃



∈ 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

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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



,1𝜆 3

(1)

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:

𝑥





𝑥





𝛼⊗𝐿é𝑣𝑦𝜆

(2)

𝑥





𝑥





𝛼𝑠⊗𝐻



𝑃



 𝜖



⊗𝑥





𝑥







(3)

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;

𝑥





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).

https://github.com/zfz/twitter_corpus/blob/master/full-cor

pus.csv

https://www.kaggle.com/crowdflower/twitter-airline-sent

iment

4 PROPOSED WORK

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.

Sanders

- 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.

https://www.kaggle.com/augustop/portuguese-tweets-for-

sentiment-analysis

Feature Selection with Hybrid Bio-inspired Approach for Classifying Multi-idiom Social Media Sentiment Analysis

301

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 as “1” and “0” 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

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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



𝑋



is the accuracy and 𝑁𝑎𝑋is the number

of selected features from subset 𝑋. They are shown in

Equations (4)-(5):

𝐹𝐹





𝐴

𝑐𝑋

(4)

𝐹𝐹





𝐴

𝑐



𝑋



1/𝑁𝑎𝑋

(5)

5 EXPERIMENTS AND RESULTS

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.

Paramete

Value

Population size (𝑃)

100

Probability CS (𝑃



)

0.25

Solution scale

(

)

Crossover prob. (𝑃



)

0.70

Mutation prob. (𝑃



)

0.10

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

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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

10.42%.

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



Classifier

Optimized only

with TF-IDF

(%)

Optimized

with GCS

(%)

Increase in

accuracy

(%)

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



Classifier

Optimized only

with TF-IDF

(%)

Optimized

with GCS

(%)

Increase in

accuracy

(%)

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

(%)

Execution

time (s)

Better

accuracy (%)

𝐹𝐹



49.54 1384.51 84.31

𝐹𝐹



37.60 3329.16 84.58

5.2 Traditional Methods Comparison

In this test, two techniques were applied, a filter and a

wrapper

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

selected

(

)

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

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Table 8: Datasets baseline accuracy with best classifier.

Dataset

Optimized only

with TF-IDF(%)

Optimized with

-Best (%)

Increase in

accuracy (%)

Optimized

with RFE (%)

Increase in

accuracy (%)

Optimized

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.

Dataset

Optimized only

with TF-IDF

Optimized

with CS

Features

selected (%)

Optimized with

Features

selected (%)

Optimized

with GCS

Features

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

datasets.

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.

𝐹𝐹



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

𝐹𝐹



over 𝐹𝐹



and

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

305

Table 10: Accuracy with ME classifier in nature inspired methods.

Dataset

Optimized only

with TF-IDF (%)

Optimized

with CS (%)

Increase in

accuracy (%)

Optimized

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).

Dataset

Optimized

with CS

Optimized

with GA

Optimized

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.

6 CONCLUSION AND FUTURE

WORK

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.

ACKNOWLEDGEMENTS

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.

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