Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis

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Comparison of Naïve Bayes, Support Vector Machine, Decision Trees
and Random Forest on Sentiment Analysis
Márcio Guia1, Rodrigo Rocha Silva2,3 a and Jorge Bernardino1,2 b
1Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal
2CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
3FATEC Mogi das Cruzes, São Paulo Technological College, 08773-600 Mogi das Cruzes, Brazil
Keywords: Data Mining, Sentiment Analysis, Text Classification, Naïve Bayes, Support Vector Machine, Random
Forest, Decision Trees.
Abstract: Every day, we deal with a lot of information on the Internet. This information can have origin from many
different places such as online review sites and social networks. In the midst of this messy data, arises the
opportunity to understand the subjective opinion about a text, in particular, the polarity. Sentiment Analysis
and Text Classification helps to extract precious information about data and assigning a text into one or more
target categories according to its content. This paper proposes a comparison between four of the most popular
Text Classification Algorithms - Naive Bayes, Support Vector Machine, Decision Trees and Random Forest
- based on the Amazon Unlocked mobile phone reviews dataset. Moreover, we also study the impact of some
attributes (Brand and Price) on the polarity of the review. Our results demonstrate that the Support Vector
Machine is the most complete algorithm of this study and achieve the highest values in all the metrics such
as accuracy, precision, recall, and F1 score.
1 INTRODUCTION
Text Mining is the process that can extract valuable
information from a text (Mouthami, Devi and
Bhaskaran, 2013). One of many applications of Text
Mining is Sentiment Analysis, which is the process
used to determine the opinion or the emotion that a
person writes about an item or topic (Mouthami, Devi
and Bhaskaran, 2013).
With the growth of the Internet, especially social
networks, people can easily express their opinion
about any topic in a few seconds, and valuable
information can be extracted from this, not only about
the person who wrote it but also about a particular
subject.
There are three categories to classify Sentiment:
Machine Learning, Lexicon-Based and an hybrid that
combines Machine Learning and Lexicon- Based
(Ahmad, Aftab and Muhammad, 2017). In literature,
the Machine Learning categories to extract Sentiment
are one of the most discussed areas and for this
reason, in this paper, we propose to do a comparison
a https://orcid.org/0000-0002-5741-6897
b https://orcid.org/0000-0001-9660-2011
between four of the most popular Machine Learning
algorithms: Naive Bayes (Kononenko, 1993),
Support Vector Machine (Cortes and Vapnik, 1995),
Decision Trees (Quinlan, 1986) and Random Forest
(Ho, 1995). In order to evaluate these classifiers, we
use Amazon Reviews: Unlocked Mobile Phones
dataset and our focus goes to the Polarity Review of
a text, which can be Negative or Positive.
The main contributions of this work are the
following:
 Compare Naive Bayes, Support Vector Machine,
Decision Trees and Random Forest on Polarity
Text Review based on Accuracy, Precision,
Recall, and F1 score;
 Compare different types of each studied
classifier models;
 Evaluate the impact of Brand and Price of the
mobile phones on final Polarity Review.
The rest of this paper is organized as follows.
Section 2 presents related work. Section 3 describes
the experimental approach. Section 4 presents the
Guia, M., Silva, R. and Bernardino, J.
Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis.
DOI: 10.5220/0008364105250531
In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 525-531
ISBN: 978-989-758-382-7
Copyright c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
525

results and discussion. Finally, Section 5 concludes
the paper and presents future work.
2 RELATED WORK
Sentiment Analysis has been utilized by many authors
to classify documents, especially with machine
learning approaches. However, the researches usually
just focus on one of the most popular machine
learning algorithms like the Support Vector Machine,
Naïve Bayes or Random Forest classifier.
(Moe et al., 2018) compares Naïve Bayes with
Support Vector Machine on Document Classification.
The authors conclude that Support Vector Machine is
more accurate than Naïve Bayes classifier.
(Xu, Li and Zheng, 2017) defend that although
Multinomial Naïve classifier is commonly used on
Text Classification with good results, it's not a fully
Bayesian Classifier. So, the authors propose a
Bayesian Multinomial Naïve Bayes classifier and the
results show that the new approach has similar
performance when compared to the classic
Multinomial Naïve Bayes classifier.
(Manikandan and Sivakumar, 2018) propose an
overview of the most popular machine learning
algorithms to deal with document classification. The
authors provide the advantages and main applications
of each algorithm. However, this paper does not
provide any practical study about the algorithms and
does not do a comparison between them.
(Rodrigues, Silva and Bernardino, 2018) propose
a new ontology to deal with social event
classification. Instead of label an event with just one
category the authors propose a classification based on
tags. So, an event can have more than one tag and this
approach can more successfully achieve the interest
of a user. To make the classification tests the authors
use the Random Forest Classifier which achieve good
results. However, to do the classification the authors
have just use one algorithm.
(Parmar, Bhanderi and Shah, 2014) study Random
Forest classifier on Sentiment Analysis. The authors
proposed an approach that tunes the hyperparameters
like number of trees to construct the Decision Forest,
number of features to select at random and depth of
each tree. They conclude that with optimized
hyperparameters the Random Forest classifier can
achieve better results. In (Text Mining Amazon
Mobile Phone Reviews: Interesting Insights, no date)
the authors of the dataset that we use in this paper
provided a statistical study about the relationship
between the attributes of the dataset and they also
extract the sentiments that are present in the reviews.
In our paper we also added some statistical study to
the one done initially by the authors of the database
by study the impact of brand and price in the polarity
review.
The main difference of these works with ours is
that we don´t focus on just one machine learning
algorithm. We propose a comparison between four
algorithms: Naïve Bayes, Support Vector Machine,
Decision Trees and Random Forest. Besides none of
these works studies the impact of the attributes of the
dataset in the classification of documents.
3 EXPERIMENTAL APPROACH
This section presents the experimental approach used
for the classification task, Fig.1 displays the overall
architecture. The proposed architecture consists of
five parts. The first one deals with cleaning the
dataset, described in section 3.1. After cleaning
dataset, we do Pre-Processing and Text
Transformation, all these steps are described in
section 3.2. In section 3.3 we describe the
classification process. The Evaluation process and the
compare of results are described in section 4.
Figure 1: Overview of our approach.
3.1 Dataset
The dataset that we use for this study (Amazon
Reviews: Unlocked Mobile Phones | Kaggle, 2016)
consists of 400 000 reviews of unlocked mobile
phones sold on Amazon.com and contains attributes
such as Brand (string), Price (real number), Rating
(integer number) and Review text (string).
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For the classification task, we only select the
Rating and Review text attributes. Rating is a
numerical value from 1 to 5, and the Review text is a
String which contains the opinion of the user. Before
using the dataset, we apply a few steps to get better
results. These steps are described as following:
1. Assign Rating value 1 and 2, to Negative;
2. Assign Rating value 4 and 5, to Positive
3. Remove all the instances that contain a Rating
value equal to 3.
3.2 Pre-processing and Text
Transformation
In order to improve results for the four algorithms that
we study in this paper, it is necessary to do some pre-
processing steps which will make it possible to reduce
data dimension without affecting the classification
task (Eler et al., 2018). The first step is to convert all
the instances of the dataset into lowercase. Next, we
remove some noisy formatting like HTML Tags and
Punctuation. Tokenization, removal of stop words
and stemming are described as follows:
- Tokenization: is the process that splits strings and
text into small pieces called tokens (Mouthami, Devi
and Bhaskaran, 2013). This process is widely used
and popular in pre-processing tasks.
- Removal of Stop Words: A stop word is a
commonly used word that appears frequently in any
document. These words are usually articles and
prepositions. An example of these terms is “the”,”
is”,” are”, “I” and “of” (Eler et al., 2018). Hence, we
can say that these terms do not add meaning to a
sentence, and for this reason, we can retrieve them
from the text before doing the classification task. For
this study, we use a list of common words of the
English Language which includes about 150 words.
- Stemming: is the process that reduces a word to their
base or root form. For example, the words
“swimmer” and “swimming” after the stemming
process are transformed into “swim”. In this study,
we use the Porter Stemmer because is one of the most
popular English rule-based stemmers (Jasmeet and
Gupta, 2016) and compared with Lovins Stemmer it´s
a more light stemmer. Moreover, produces the best
output as compared to other stemmers (Ganesh
Jivani, 2011).
Text Transformation: Machine learning
algorithms do not work with text features, so, for this
reason, we need to convert text into numerical
features. To deal with that, we use the TF-IDF (Term
Frequency-Inverse Document Frequency). This
algorithm assigns to each word of the sentence a
weight based on the TF and IDF (Yang and Salton,
1973).
The TF (term frequency) of a word is defined as
the number of times that the word appears in a
document.
The IDF (inverse document frequency) of a term
is defined as how important a term is (Salton and
Buckley, 1988) (Yang and Salton, 1973).
3.3 Classification Process
After cleaning the dataset and apply pre-processing
and text transformation steps, we split the data into
training and test. The percentage used for training is
80% and the remaining 20% are used for test. It is
necessary to feed the classification algorithms, so the
train data will be used for training the classifiers and
the test data will be used to evaluate them. The four
classifiers that we use are described in the following:
- Random Forest: is defined as a classifier with a
collection of tree-structured classifiers {h(x, k ), k =
1,...} where the {k} are independent identically
distributed random vectors and each tree casts a unit
vote for the most popular class at input x. When a
large number of trees is generated each one of them
will vote for a class, and the winner is the class that
has more votes (Breiman, 2001). For this study we
evaluate the Random Forest classifier with a different
number of trees to construct the Decision Forest, in
particular, we test the classifier with 50,100,200 and
400 trees.
-Naive Bayes: is a probabilistic machine learning
classifier based on the Bayes Theorem with an
assumption of independence among predictors, in
other words, this algorithm considers that a presence
of a feature in a class is independent of any other
features (Ahmad, Aftab and Muhammad, 2017). For
this study we evaluate two types: Multinomial and
Bernoulli.
Support Vector Machine: is a supervised learning
model which can achieve good results in text
categorization. Basically this classifier locates the
best possible boundaries to separate between positive
and negative training samples (Ahmad, Aftab and
Muhammad, 2017) For this study, we evaluate two
distinct kernel models for Support Vector Machine:
RBF and Linear (Minzenmayer et al., 2014) .
Decision Trees: is an algorithm that use trees to
predict the outcome of an instance. Essentially, a test
node computes an outcome based on the attribute
values of an instance, where each possible outcome is
associated with one of the subtrees. The process of
classify an instance starts on the root node of the tree.
If the root node is a test, the outcome for the instance
Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis
527

it is predicted to one of the subtrees and the process
continues until a leaf node it is encountered, in this
situation the label of the leaf node gives the predicted
class of the instance (Quinlan and Quinlan J. R.,
1996).
4 EXPERIMENTAL
EVALUATION
We use the Amazon Reviews: Unlocked Mobile
Phones dataset (Amazon Reviews: Unlocked Mobile
Phones | Kaggle,2016) and we split the dataset into
80 % for train and 20% for the test. As mentioned,
before we provide a comparison between four
algorithms and also offer a statistical about the impact
of the brand and the price in the final polarity review.
These experiments are described as follows:
4.1 Algorithms Classification
In order to evaluate the results of the four algorithms
we use four of the most popular measures: Accuracy,
Precision, Recall, and F1 score. These four metrics
are explained in the following:
- Accuracy: is the most popular measure and also very
easy to understand because is a simple ratio between
the number of instances correctly predicted to the
total number of instances used in the observation, in
other words, accuracy gives the percentage of
correctly predicted instances (Mouthami, Devi and
Bhaskaran, 2013).
- Precision: is a measure that provides for each class
the ratio between correctly positive predicted
instances and total of positive instances predicted
(Mouthami, Devi and Bhaskaran, 2013).
- Recall: is a measure that provides for each class the
ratio between the true positive instances predicted and
the sum of true positives and false negatives in the
observation (Mouthami, Devi and Bhaskaran, 2013).
- Fl score: is the weighted average of Precision and
Recall (Mouthami, Devi and Bhaskaran, 2013), and
it's considered perfect when it´s 1.0 and the worst
possible value is 0.0, so a good F1 score means that
we have low false positives and low false negatives.
4.2 Naive Bayes
Table 1 shows the results of application Naive Bayes
on the dataset. The first experimental for the Naive
Bayes classifier was the Multinomial variant. The
results demonstrated that the classifier obtains 0.83
which means that in 83% of times the polarity reviews
was correctly predicted. Precision and Recall obtain
similar values, 0.84 and 0.83 respectively, F1 score
obtains 0.80. The second experimental was with
Bernoulli variant and the results show an
improvement of 2% for Accuracy and Recall and 4%
for F1 score.
In conclusion, the two variants of Naive Bayes
can both achieve good results in Sentiment Analysis
especially the Bernoulli Variant. However, the Naive
Bayes classifier when compared to Random Forest
and especially Support Vector Machine obtain
modest results.
Table 1: Results for the measures of application Naïve
Bayes on the dataset.
Accuracy
Precision
Recall
F1
score
Multinomial
0.83
0.84
0.83
0.80
Bernoulli
0.85
0.84
0.85
0.84
4.3 Random Forest
Table 2 shows the results of application Random
Forest on the dataset. When the number of estimators
was 50 the classifier obtains 0.87 for Accuracy,
Precision, Recall and F1 score, which can be
considered a good result considering the small
number of estimators. When the numbers of
estimators were 100 the results demonstrate an
increment of 1% for Accuracy and Recall, and the
Precision and F1 score remained the same values. The
results for the third experimental test with 200
estimators for the Random Forest classifier
demonstrate that Precision achieves 0.88 which is
more 0.1% than the experimental with 100. Finally,
in the last experimental, the number of estimators was
400 and the results show that with this high number
of estimators the results for all the measures are still
equal to the experiment with 200 estimators.
In conclusion, the results for the application of
Random Forest classifier show that this algorithm can
achieve high values for all the measures even when
the number of estimators is low, it means that
Random Forest can be used with success on text
classification tasks. It is also possible to conclude that
when the number of estimators increases the
Precision, Recall and Accuracy also increases.
However, the best result of Random Forest was with
200 estimators. Increasing the number of estimators
did not achieve better results.
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Table 2: Results for the measures of application Random
Forest on the dataset.
Accuracy
Precision
Recall
F1
score
50
estimators
0.87
0.87
0.87
0.87
100
estimators
0.88
0.87
0.88
0.87
200
estimators
0.88
0.88
0.88
0.87
400
estimators
0.88
0.88
0.88
0.87
4.4 Support Vector Machine
Table 3 shows the results of application Support
Vector Machine on the dataset. As mentioned before
we use two types of kernel models to evaluate the
Support Vector Machine. The first experimental
evaluation demonstrates that with Linear kernel, the
classifier obtains 0.89 for Accuracy, Precision, Recall
and F1 score which means that 89% of the times the
classifier predicted correctly the polarity of a review.
The second experimental demonstrates that with RBF
Kernel the results obtained are significantly lower
than the results with Linear Kernel, namely, the
results for Accuracy and Recall decrease 16 %, the
value of Precision drastically decreases 36 % and the
value of F1 score decreases 28%.
In conclusion, the Support Vector Machine with
Linear Kernel achieves the best results of this study
and proves that it is one of the best algorithms to deal
with Sentiment Analysis. However, the poor results
of the application of Support Vector Machine with
RBF kernel demonstrate that the latter it is not a good
classifier for Sentiment Analysis.
Table 3: Results for the measures of application Support
Vector Machine on the dataset.
Accuracy
Precision
Recall
F1 score
Linear
0.89
0.89
0.89
0.89
RBF
0.73
0.53
0.73
0.61
4.5 Decision Trees
Table 4 shows the results of the application of
Decision Trees on the dataset. The results show that
the Decision Trees classifier obtains the same value
(0.82) for all the four measures: Accuracy, Precision,
Recall, and F1 score. These results are similar to the
Multinomial Naive Bayes and we can conclude that
Naive Bayes and Decision Trees achieve similar
values in the Sentiment Analysis task which can be
explained by the lower complexity of these two
algorithms when compared to Random Forest and
Support Vector Machine.
Table 4: Results for the measures of application Decision
Trees on the dataset.
Accuracy
Precision
Recall
F1
score
Decision
Trees
0.82
0.82
0.82
0.82
4.6 Impact of Brand and Price
In this study, we also make a statistical comparison of
the impact of attributes (brand and price) in the final
polarity review. For brand, we study the most popular
brands of phones that are present in the dataset and
for price we provide an overview of all the prices that
are presented in the dataset.
4.6.1 Brand
Table 5 shows the impact of the brand in the polarity
review. After having analyzed these results we
conclude that the impact of the brands is similar and
is in a range of 77% to 79%. However, there are two
brands which stand out from the rest. The first one is
the BlackBerry with only 74.3 % positive reviews.
The second one is ZTE which has the best results with
82.9% positive reviews. We think that the significant
difference in the percentage of positive reviews
between BlackBerry and ZTE could be explained by
a phone model from BlackBerry that has the potential
to give problems or does not match customer
expectations and the high results of ZTE can be
explained by the fewer models that are present in the
dataset.
Table 5: Results for the impact of the brand on polarity
review.
Brand
% of reviews
Positive
Negative
Samsung
79.94
20.06
Apple
77.3
22.7
Nokia
78.01
21.99
BlackBerry
74.3
25.7
Asus
77.41
22.59
LG
77.2
22.8
Sony
79.86
20.14
ZTE
82.9
17.1
Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis
529

4.6.2 Price
Table 6 shows the impact of the price in the polarity
review. After having analyzed these results we
conclude that there’s a significant difference between
the range of fewer than 100 dollars (73.2 % of
positive reviews) and the range of 1000 to 1500
dollars ( 84.3% of positive reviews). It's also possible
to conclude that as the price range increase the
percentage of positive reviews also increases
reaching the maximum in the range of 1000 to 1500
after that the percentage of positive reviews falls by
one percentage point to 83.3 %. These results can be
explained by the quality of the phones, it means that
products with a lower price may have less quality than
products with high price, which have more features
and also more quality. Hence it is expected that as the
price increases the percentage of positive reviews also
increases.
Table 6: Results for the impact of price on polarity review.
Price (Dollars)
% of reviews
Positive
Negative
Less than 100
73.2
26.8
100 to 200
76.8
23.2
200 to 300
79.1
20.9
300 to 400
79.2
20.8
400 to 500
81.4
18.6
500 to 1000
81.4
18.6
1000 to 1500
84.3
15.7
1500 to 2000
83.3
16.7
Above 2000
83.3
16.7
5 CONCLUSIONS AND FUTURE
WORK
In this paper, we analyzed four of the most popular
machine learning algorithms to deal with Sentiment
Analysis, based on four measures: Accuracy,
Precision, Recall, and F1 score. We found that the
Support Vector Machine classifier is not only the
most accurate of this study but also the most complete
classifier with high values to all the measures. Our
results show that Random Forest is also a classifier to
take into account and can achieve high values to all
the measures being just slightly worse than the
Support Vector Machine classifier.
This study also proposes a statistical study about
the impact of brand and price in the polarity review
and concludes with some interesting facts about each
one of these attributes. For the brand, we can have an
overview of the impact of each brand in the polarity
review and concluded that ZTE is the brand with the
most positive reviews with 82.9 %, as opposed to
BlackBerry with just only 74.3 %. For the price, we
can conclude that as the price increases the
percentage of positive reviews also increases,
reaching a maximum of positive reviews in the range
of 1000 to 1500 dollars after that the percentage of
positive reviews falls from 84.3% to 83.3 %.
As future work, we plan to continue the study of
other algorithms that are usually applied to Sentiment
Analysis and evaluate them with the measures that we
used in this study. We also plan to propose an
architecture to improve the results of each one of the
four algorithms that we evaluated and compared in
this study.
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