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Sentiment Analysis in English Texts
Arwa Alshamsi1, Reem Bayari1, Said Salloum2,3,*
1Faculty of Engineering & IT, The British University, Dubai, 345015, UAE
2Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, 27272, UAE
3School of Science, Engineering, and Environment, University of Salford, Manchester, M5 4WT, UK
A R T I C L E I N F O
A B S T R A C T
Article history:
Received: 23 September, 2020
Accepted: 24 December, 2020
Online: 28 December, 2020
The growing popularity of social media sites has generated a massive amount of data that
attracted researchers, decision-makers, and companies to investigate people's opinions and
thoughts in various fields. Sentiment analysis is considered an emerging topic recently.
Decision-makers, companies, and service providers as well-considered sentiment analysis
as a valuable tool for improvement. This research paper aims to obtain a dataset of tweets
and apply different machine learning algorithms to analyze and classify texts. This research
paper explored text classification accuracy while using different classifiers for classifying
balanced and unbalanced datasets. It was found that the performance of different classifiers
varied depending on the size of the dataset. The results also revealed that the Naive Byes
and ID3 gave a better accuracy level than other classifiers, and the performance was better
with the balanced datasets. The different classifiers (K-NN, Decision Tree, Random Forest,
and Random Tree) gave a better performance with the unbalanced datasets.
Keywords:
Sentiment Analysis
Balanced Dataset
Unbalanced Dataset
Classification
1. Introduction
The recent widening expansion of social media has changed
communication, sharing, and obtaining information [1–4]. In
addition to this, many companies use social media to evaluate their
business performance by analysing the conversations' contents [5].
This includes collecting customers' opinions about services,
facilities, and products. Exploring this data plays a vital role in
consumer retention by improving the quality of services [6, 7].
Social media sites such as Instagram, Facebook, and Twitter offer
valuable data that can be used by business owners not only to track
and analyse customers' opinions about their businesses but also
that of their competitors [8–11]. Moreover, these valuable data
attracted decision-makers who seek to improve the services
provided [8, 9, 12, 13].
In this research paper, several research papers that studied
Twitter's data classification and analysis for different purposes
were surveyed to investigate the methodologies and approaches
utilized for text classification. The authors of this research paper
aim to obtain open-source datasets then conduct text classification
experiments using machine learning approaches by applying
different classification algorithms, i.e., classifiers. The authors
utilized several classifiers to classify texts of two versions of
datasets. The first version is unbalanced datasets, and the second
is balanced datasets. The authors then compared the classification
accuracy for each used classifier on classifying texts of both
datasets.
2. Literature Review
As social media websites have attracted millions of users,
these websites store a massive number of texts generated by users
of these websites [14–21]. Researchers were interested in
investigating these metadata for search purposes [17, 18, 22–25].
In this section, a number of research papers that explored the
analysis and classification of Twitter metadata were surveyed to
investigate different text classification approaches [26] and the text
classification results.
Researchers of [27] investigated the user's gender of Twitter.
Authors noticed that many Twitter users use the URL section of
the profile to point to their blogs, and the blogs provided valuable
demographic information about the users. Using this method, the
authors created a corpus of about 184000 Twitter users labeled
with their gender. Then authors arranged the dataset for
ASTESJ
ISSN: 2415-6698
*Corresponding Author: Said Salloum, University of Sharjah, UAE. Tel:
+971507679647 Email: ssalloum@sharjah.ac.ae
Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 1683-1689 (2020)
www.astesj.com
Special Issue on Multidisciplinary Sciences and Engineering
https://dx.doi.org/10.25046/aj0506200
H. Tariq et al. / Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 1683-1689 (2020)
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experiments as following: for each user; they specify four fields;
the first field contains the text of the tweets and the remaining three
fields from the user's profile on Twitter, i.e., full name, screen
name, and description. After that, the authors conducted the
experiments and found that using all of the dataset fields while
classifying Twitter user's gender provides the best accuracy of
92%. Using tweets text only for classifying Twitter user's gender
provides an accuracy of 76%. In [28], the authors used Machine
Learning approaches for Sentiment Analysis. Authors constructed
a dataset consisting of more than 151000 Arabic tweets labeled as
"75,774 positive tweets and 75,774 negative tweets". Several
Machine Learning Algorithms were applied, such as Naive Bayes
(NB), AdaBoost, Support vector machine (SVM), ME, and Round
Robin (RR). The authors found that RR provided the most accurate
results on classifying texts, while AdaBoost classifier results were
the least accurate results. A study by [29] interested as well in
Sentiment Analysis of Arabic texts. The authors constructed the
Arabic Sentiment Tweets Dataset ASTD, which consists of 84,000
Arabic tweets. The number of tweets remaining after annotation
was around 10,000 tweets. The authors applied machine learning
approaches using classifiers on the collected dataset. They reported
the following: (1) The best classifier applied on the dataset is SVM,
(2) Classifying a balanced set is challenging compared to the
unbalanced set. The balanced set has fewer tweets than the
unbalanced set, which may negatively affect the classification's
reliability. In [30], the author investigated the effects of applying
preprocessing methods before the sentiment classification of the
text. The authors used classifiers and five datasets to evaluate the
preprocessing method's effects on the classification. Experiments
were conducted, and researchers reported the following findings:
Removing URL has no much effect, Removing stop words have a
slight effect, Removing Numbers have no effect, Expanding
Acronym improved the classification performance, and the same
preprocessing methods have the same effects on the classifier's
performance, NB and RF classifiers showed more sensitivity than
LR and SVM classifiers. In conclusion, the classifier's
performance for sentiment analysis was improved after applying
preprocessing methods. A study by [31] investigated Twitter
geotagged data to construct a national database of people's health
behavior. The authors compared indicators generated by machine
learning algorithms to indicators generated by a human. The
authors collected around 80 million geotagged tweets. Then
Spatial Join procedures were applied, and 99.8% of tweets were
successfully linked. Then tweets were processed. After that,
machine learning approaches were used and successfully applied
in classifying tweets into happy and not happy with high accuracy.
In [32] explored classifying sentiments in movie reviews. The
authors constructed a dataset of 21,000 tweets of movie reviews.
Dataset split into train set and test set. Preprocessing methods
applied, then two classifiers, i.e., NB and SVM, were used to
classify tweets text into positive or negative sentiment. The authors
found that better accuracy achieved using SVM of 75% while NB
has 65% accuracy. Researchers of [33] used Machine Learning
methods and Semantic Analysis for analyzing tweet's sentiments.
Authors labeled tweets in a dataset that consists of 19340 sentences
into positive or negative. They applied preprocessing methods
after that features were extracted; authors applied Machine
Learning approaches, i.e., Naïve Bayes, Maximum Entropy, and
Support Vector Machine (SVM) classifiers after that Semantic
Analysis were applied. The authors found that Naïve Bayes
provided the best accuracy of 88.2, the next SVM of 85.5, and the
last is Maximum entropy of 83.8. The authors reported as well that
after applying Semantic Analysis, the accuracy increased to reach
89.9. In [34], the authors analyzed sentiments by utilizing games.
Authors introduced TSentiment, which is a web-based game.
TSentiment used for emotion identification in Italian tweets.
TSentiment is an online game in which the users compete to
classify tweets in the dataset consists of 59,446 tweets. Users first
must evaluate the tweet's polarity, i.e., positive, negative, and
neutral, then users have to select the tweet's sentiment from a pre-
defined list of 9 sentiments in which 3 sentiments identified for the
positive polarity, 3 sentiments identified for negative polarity.
Neutral polarity is used for tweets that have no sentiment
expressions. This approach for classifying tweets was effective.
A study by [35] examined the possibility of enhancing the
accuracy of predictions of stock market indicators using Twitter
data sentiment analysis. The authors used a lexicon-based
approach to determine eight specific emotions in over 755 million
tweets. The authors applied algorithms to predict DJIA and
S&P500 indicators using Support Vectors Machine (SVM) and
Neural Networks (NN). Using the SVM algorithm in DJIA
indication, the best average precision rate of 64.10 percent was
achieved. The authors indicated that the accuracy could be
increased by increasing the straining period and by improving the
algorithms for sentiment analysis. authors conclude that adding
Twitter details does not improve accuracy significantly. In [36],
the authors applied sentiment analysis on around 4,432 tweets to
collect opinions on Oman tourism, they build a domain-specific
ontology for Oman tourism using Concept Net. Researchers
constructed a sentiment lexicon based on three existing lexicons,
SentiStrength, SentWordNet, and Opinion lexicon. The authors
randomly divide 80% of the data for the training set and 20% for
testing. The researcher used two types of semantic sentiment,
Contextual Semantic Sentiment Analysis, and Conceptual
Semantic Sentiment Analysis. Authors applied Nave Base
supervised machine learning classifier and found that using
conceptual semantic sentiment analysis expressively improves the
sentiment analysis's performance. A study by [37] used sentiment
analysis and subjectivity analysis methods to analyze French
tweets and predict the French CAC40 stock market. The author
used a French dataset that consists of 1000 positive and negative
book reviews. The author trained the neural network by using three
input features on 3/4 of the data, and he tested on the remaining
quarter. The achieved accuracy 80% and a mean absolute
percentage error (MAPE) of 2.97%, which is less than the work
reported by Johan Bollen. The author suggested adding more
features as input to improve the performance. In [38], the authors
examined the relationship between Twitter's social emotion and
the stock market. Researchers collected millions of tweets by
Twitter API. Researchers retrieved the NASDAQ market closing
price in the same period. The authors applied the correlation
coefficient. Authors conclude that emotion-related terms have
some degree of influence on the stock market overall trend, but it
did not meet standards that can be used as a guide to stock-market
prediction. While at the same time, there was a fairly close
association between positive, negative, and angry mood-words.
Particularly sad language tends to have a far greater influence on
the stock market than other groups. In [39], the authors
investigated telecommunications companies' conversation on
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social media Twitter ('indihome,' in Indonesia ). The authors
collected 10,839 raw data for segmentation. The authors collected
data: over 5 periods of time in the same year. Authors found that
most of the tweets (7,253) do not contain customers' perception
toward Indihome. Only 3,586 tweets are containing the perception
of customers toward Indihome. Most of the data contained
perception reveal that the customers have the negative perception
(3,119) on Indihome and only 467 tweets contain positive
perceptions; the biggest number of negative perceptions relate to
the first product, the second relates to a process, third relate to
people, and fourth relate to pricing. Researchers of [40] examined
prevalence and geographic variations for opinion polarities about
e-cigarettes on Twitter. Researchers collected data from Twitter by
pre-defined seven keywords. They classified the tweets into four
categories: Irrelevant to e-cigarettes, Commercial tweets, organic
tweets with attitudes (supporting or against or neutral) the use of
e-cigarettes, and the geographic locations information city and
state. Researchers selected six socio-economic variables from
Census data 2014 that are associated with smoking and health
disparities. Researchers classified the tweets based on a
combination of human judgment and machine-learning
algorithms, and two coders classified a random sample of 2000
tweets into five categories. The researcher applied a multilabel
Nave Bayes classifier algorithm; the model achieved an accuracy
of 93.6% on the training data. Then the researcher applied the
machine learning algorithm to a full set of collected tweets and
found the accuracy of the validation data was 83.4%. To evaluate
the socio-economic impact related to public perception regarding
e-cigarette use in the USA, researchers calculated the Pearson
correlation between prevalence and percentage of opinion
polarities and selected ACS variants for 50 states and the District
of Columbia. In [41], the authors Investigated the link between any
updates on certain brands and their reaction. Researchers gathered
geographic locations based on the data to see consumer
distribution. Researchers collected Twitter data by using the REST
API. In total, 3,200, from ten different profiles, then used
sentiment analysis to differentiate between clustered data
expressed positively or negatively then resampled the result in an
object model and cluster. For every answer, the researcher has been
evaluated for the textual sentiment analysis from the object model.
Researchers used AFINN based word list and Sentiments of
Emojis to run comprehensive sentiment analysis; for the data that
not existed in the word list, researcher added a separated layer to
an analysis by using emoji analysis on top of sentiment analysis,
and authors did not see any difference in the level of accuracy
when applying this extra layer. The researcher found some
Sentiment Analysis weaknesses related to the misuse of emoji, the
use of abbreviated words or terms of slang, and the use of sarcasm.
In [42], the authors proposed an application that can classify a
Twitter content into spam or legitimate. Auhtors used an integrated
approach, from URL analysis, Natural Language Processing, and
Machine Learning techniques. Auhtors analyzed the URL that
derived from the tweets, then convert URLs to their long-form,
then compare URLs with Blacklisted URLs, then compare them
with a set pre-defined expressions list as spam; the presence of any
of these expressions can conclude that the URL is spam. After
cleaning data, the stemmed keywords are compared with the per
set of identified spam words and, if a pre-defined expressions list
are found in the tweet, then the user is classified as spam. Six
features were used for classification. The training set has 100
instances with six features and a label. The author used Nave-
Bayes algorithm. Authors manually examined 100 users and found
(60 were legitimate and 40 were spam) then the sampled checked
by the application and the result presented that 98 were classified
correctly.
3. Proposed Approach
In this work, the authors implemented and evaluated different
classifiers in classifying the sentiment of the tweets. It’s by
utilizing RapidMiner software. Classifiers were applied on both
balanced and unbalanced datasets. Classifiers used are Decision
Tree, Naïve Bayes, Random Forest, K-NN, ID3, and Random
Tree.
4. Experiment Setup
In this section, the dataset is described as well as the settings
and evaluation techniques are used in the experiments have been
discussed. The prediction for the tweet category is tested twice—
the first time on an unbalanced data set and the second time on a
balanced dataset as below.
• Experiments on the unbalanced dataset: Decision Tree,
Naïve Bayes, Random Forest, K-NN, ID3, and Random Tree
classifiers were applied on six unbalanced datasets.
• Experiments on the balanced dataset: In this experiment,
the challenges related to unbalanced datasets were tackled by
manual procedures to avoid biased predictions and misleading
accuracy. The majority class in each dataset almost equalized
with the minority classes, i.e., many positive, negative, and
neutral, practically the same in the balanced dataset as
represented in Table 3.
4.1. Dataset Description
We obtained a dataset from Kaggle, one of the largest online
data science communities in this work. It consists of more than
14000 tweets, labeled either (positive, negative, or neutral). The
dataset was also split into six datasets; each dataset includes tweets
about one of six American airline companies (United, Delta,
Southwest, Virgin America, US Airways, and American). Firstly,
we summarized the details about the obtained datasets, as
illustrated in Table 1 below.
Table 1: Summary of obtained Dataset
American Airline Companies
Virgin
Americ
a
Unite
d
Delt
a
Southwes
t
US
Airway
s
America
n
Number
of
Tweets
504
3822
2222
2420
2913
2759
Positive
Tweets
152
492
544
570
269
336
Negativ
e
Tweets
181
2633
955
1186
2263
1960
Neutral
Tweets
171
697
723
664
381
463
4.2. Dataset Cleansing
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In this section, the authors described the followed procedure
in the dataset preparation. The authors utilized RapidMinor
software for tweet classification. Authors followed the methods
described below:
1) Splitting the dataset into a training set and test set.
2) Loading the dataset, i.e., excel file into RapidMinor software
using Read Excel operator.
3) Applying preprocessing by utilizing the below operators.
• Transform Cases operator to transform text to lowercase.
• Tokenize operator to split the text into a sequence of tokens.
• Filter Stop words operator to remove stop words such as: is,
the, at, etc.
• Filter Tokens (by length) operator: to remove token based on
the length, in this model, minimum characters are 3, and
maximum characters are 20 any other tokens that don't match
the rule will be removed.
• Stem operator: to convert words into base form.
4.3. Dataset Training
Each of the datasets was divided into two-part. The first part
contains 66% of the total number of tweets of the data set, and it is
used to train the machine to classify the data under one attribute,
which is used to classify the tweets to either (positive or Negative
or Neutral). The remaining 34% of tweets were used to classify
tweets' attribute to (positive or Negative or Neutral), i.e., test set.
Figure 1: Summarization of the Process Model
4.4. Dataset Classifying
In this section, the authors described the steps in the tweet’s
classification techniques.
• Set Role operator is used to allow the system to identify
sentiment as the target variable,
• Select Attributes operator is used to removing any attribute
which has any missing values.
• Then in the validation operator, the dataset is divided into two
parts (training and test). We used Two-thirds of the dataset to
train the dataset and the last one-third to evaluate the model.
• Different machine learning algorithms are used for training
the dataset (Decision Tree, Naïve Bayes, Random Forest, K-
NN, ID3, and Random Tree).
• For testing the model, the Performance operator utilized to
measure the performance of the model.
5. Experiment Results and Discussion
This section presented the experiment results in terms of
accuracy level of prediction for each classifier on both types of
datasets (balanced, unbalanced) and a comparison between the two
experiments.
5.1. Experiment results for an unbalanced dataset
Figure 2 and Table 2 present the accuracy results of the
utilized classifiers on the datasets.
Table 2: Accuracy results on unbalanced dataset
Accuracy
Virgin
Americ
a
United
Delta
Southw
est
US
Airway
s
Americ
an
Dataset
504
3822
2222
2420
2913
2759
Training
set
333
2523
1467
1597
1923
1821
Test set
171
1299
755
823
990
938
Decision
Tree
31.86%
72.03%
42.08%
50.46%
82.72%
68.98%
Naïve
Bayes
32.74%
72.38%
42.28%
51.01%
82.72%
72.21%
Random
Forest
31.86%
72.03%
42.08%
50.46%
82.72%
68.98%
K-NN
39.82%
11.66%
35.27%
50.46%
82.72%
69.43%
ID3
32.74%
72.38%
42.28%
51.01%
82.72%
72.21%
Random
Tree
31.86%
72.03%
42.08%
50.46%
82.72%
68.98%
Figure 2: Accuracy results on unbalanced airline datasets using different
classifiers
In some datasets, the classifier's accuracy results were very
high, while it was low in others. All classifier's performance on the
US airways dataset and United dataset provided the best accuracy
due to the dataset's size, which was the largest. Naïve Bayes
classifier, Decision Tree, and ID3 were mostly better than other
classifiers and were given almost the same accuracy level. The
classifiers with Virgin America dataset reported the lowest
accuracy level due to the dataset's size, which is very small.
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5.2. Experiment results for a balanced dataset
Decision Tree, Naïve Bayes, Random Forest, K-NN, ID3, and
Random Tree classifiers were applied on the five obtained
balanced datasets. (United, Delta, Southwest, and US Airways).
The dataset for each was divided into two parts. The first part
contains 66% of the total number of tweets of the data set, and it is
used to train the machine to classify the data under one attribute,
which is used to classify the tweets as either positive, Negative, or
Neutral. The remaining 34% of tweets were used to classify tweets'
attributes into (positive, Negative, or Neutral), i.e., test set.
Table 3: Number of tweets before and after balancing.
Number of instances
Percentage
Total
tweets
before
balancing
Total
tweets
after
balancing
Positive
Negative
Neutral
United
3822
8276
33%
33%
34%
Delta
2222
2635
33%
33%
34%
Southwest
2420
5518
33%
33%
33%
US Airways
2913
6608
33%
33%
33%
American
2759
5924
34%
34%
33%
After applying different algorithms on the five balanced
datasets, the performance, i.e., accuracy results, were reported in
Table 4 and Figure 3 below:
Table 4: Accuracy results on the balanced dataset
Accuracy
Virgin
Americ
a
United
Delta
Southw
est
US
Airway
s
Americ
an
Dataset
8276
2635
5518
6608
5924
8276
Training
set
5464
1743
3642
4363
3911
5464
Test set
2812
892
1876
2245
2013
2812
Decision
Tree
35.06%
34.63%
34.35%
35.06%
33.98%
35.06%
Naïve
Bayes
97.65%
36.99%
65.48%
97.65%
61.20%
97.65%
Random
Forest
35.06%
34.63%
34.35%
35.06%
33.98%
35.06%
K-NN
38.79%
32.77%
35.32%
38.79%
39.47%
38.79%
ID3
97.65%
36.99%
65.48%
97.65%
61.20%
97.65%
Random
Tree
35.06%
34.63%
34.35%
35.06%
33.98%
35.06%
Figure 3: Accuracy results on balanced airline datasets using different classifiers
5.3. Comparison between two experiments results for each
classifier
While comparing results between the performance of the
classifiers on balanced and unbalanced datasets, it was found the
following as seen in Figure 4 below:
5.3.1 Naive Byes and ID3
Gave the best accuracy than other classifiers in the two
experiments. The accuracy level with the balanced datasets higher
than unbalanced ones. In the unbalanced datasets, the maximum
accuracy for both classifiers was 82.7%. In the balanced dataset,
the accuracy reached 97.6%; these results confirm that these two
classifiers are the best compared to the other selected classifiers in
the two experiments:
5.3.2 K-NN and Decision Tree
Show better performance with the unbalanced datasets, and
the difference is so apparent. The maximum accuracy with the
balanced datasets is 39.4%, while it reached 82.7 % with the
unbalanced datasets.
5.3.3 Random forest and Random Tree
It shows better performance with the unbalanced datasets, and
the difference is so apparent. The maximum accuracy with the
balanced datasets around 35%, while it reached 82.7% with the
unbalanced datasets.
In conclusion, Naive Bayes and ID3 gave a better accuracy
level than other classifiers, and the performance was better with
the balanced datasets. The different classifiers (K-NN, Decision
Tree, Random Forest, and Random Tree) gave a better
understanding of the unbalanced datasets.
Figure 4: Accuracy results of classifiers on balanced and unbalanced datasets
6. Conclusions
Social media websites are gaining very big popularity among
people of different ages. Platforms such as Twitter, Facebook,
Instagram, and Snapchat allowed people to express their ideas,
opinions, comments, and thoughts. Therefore, a huge amount of
data is generated daily, and the written text is one of the most
common forms of the generated data. Business owners, decision-
makers, and researchers are increasingly attracted by the valuable
and massive amounts of data generated and stored on social media
websites. Sentiment Analysis is a Natural Language Processing
field that increasingly attracted researchers, government
authorities, business owners, services providers, and companies to
improve products, services, and research. In this research paper,
the authors aimed to survey sentiment analysis approaches.
Therefore, 16 research papers that studied Twitter's text
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classification and analysis were surveyed. The authors also aimed
to evaluate different machine learning algorithms used to classify
sentiment to either positive or negative, or neutral. This experiment
aims to compare the efficiency and performance of different
classifiers that have been used in the sixteen papers that are
surveyed. These classifiers are (Decision Tree, Naïve Bayes,
Random Forest, K-NN, ID3, and Random Tree). Besides, the
authors investigated the balanced dataset factor by applying the
same classifiers twice on the dataset, one on the unbalanced and
the other, after balancing the dataset. The targeted dataset included
six datasets about six American airline companies (United, Delta,
Southwest, Virgin America, US Airways, and American); it
consists of about 14000 tweets. The authors reported that the
classifier's accuracy results were very high in some datasets while
low in others. The authors indicated that the dataset size was the
reason for that. On the balanced dataset, the Naïve Bayes classifier,
Decision Tree, and ID3 were mostly better than other classifiers
and have given the almost same level of accuracy. The classifiers
with Virgin America dataset reported the lowest level of accuracy
due to its small size. On the unbalanced dataset, results show that
the Naive Byes and ID3 gave a better level of accuracy than other
classifiers when it’s applied on the balanced datasets. While (K-
NN, Decision Tree, Random Forest, and Random Tree) gave a
better understanding of the unbalanced datasets.
Conflict of Interest
The authors declare no conflict of interest
Acknowledgment
This is a part of project done in British University in Dubai.
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