Comparison of Naïve Bayes, Support Vector Machine, Decision Trees

and Random Forest on Sentiment Analysis

Márcio Guia

, Rodrigo Rocha Silva

2,3 a

and Jorge Bernardino

1,2 b

Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal

CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal

FATEC 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

https://orcid.org/0000-0002-5741-6897

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

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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526

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

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.

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

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Table 2: Results for the measures of application Random

Forest on the dataset.

Accuracy

Precision

Recall

score

estimators

0.87

100

estimators

0.88

0.87

0.88

0.87

200

estimators

0.88

0.87

400

estimators

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

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

score

Decision

Trees

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

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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