The International Arab Conference on Information Technology (ACIT’2015)
A Prototype for a Standard Arabic Sentiment Analysis
Corpus
Mohammed N. Al-Kabi
Computer Science Department
Zarqa University
P. O. Box 2000
13110 Zarqa -Jordan
malkabi@zu.edu.jo
Mahmoud A. Al-Ayyoub
Computer Science Department
Jordan University of Science and Technology
Irbid, Jordan
maalshbool@just.edu.jo
Izzat M. Alsmadi
Computer Science Department
University of New Haven
West Haven, CT 06516, USA
ialsmadi@newhaven.edu
Heider A. Wahsheh
Computer Science Department
College of Computer Science
King Khaled University
Abha, Saudi Arabia
heiderwahsheh@yahoo.com
Abstract: The researchers in the field of Arabic sentiment analysis (SA) need a relatively big standard Arabic sentiment
analysis corpus to conduct their studies. There are a number of existing Arabic datasets; however they suffer from certain
limitations such as the small number of reviews or topics they contain, the restriction to Modern Standard Arabic (MSA), or
not being publicly available. Therefore, this study aims to establish a flexible and relatively big standard Arabic sentiment
analysis corpus that can be considered as a pillar and cornerstone to build larger Arabic corpora. In addition to MSA, this
corpus contains reviews written in the five main Arabic dialects (Egyptian, Levantine, Arabian Peninsula, Mesopotamian, and
Maghrebi group). Furthermore, this corpus has other five types of reviews (English, mixed MSA & English, French, mixed
MSA & Emoticons, and mixed Egyptian & Emoticons). This corpus is released for free to be used by researchers in this field,
where it is characterized by its flexibility in allowing the users to add, remove, and revise its contents. The total number of
topics and reviews of this initial copy is 250 and 1,442, respectively. The collected topics are distributed equally among five
domains (classes): Economy, Food-Life style, Religion, Sport, and Technology, where each domain has 50 topics. This corpus
is built manually to ensure the highest quality to the researchers in this field.
Keywords: Sentiment analysis; opinion mining; making of Arabic corpus; Arabic reference corpus; Maktoob Yahoo!
1. Introduction
The Arabic language is a Semitic language
originated in the Arabian Peninsula. It is one of the first
common Semitic languages, such as Amharic, Hebrew,
Tigrinya, and Aramaic. The Modern Standard Arabic
(MSA) is the language mainly used in the media
(Radio, TV, news bulletin, books, journals, newspapers,
ads, etc.) and it is the language used in education and
official correspondence. MSA dates back to the end of
the eighteenth century, and it is the official language of
27 countries worldwide. These countries are located in
the Arab world spanning the regions from Southwest
Asia to Northwest Africa including the horn of Africa.
There is no consensus on the total number of Arabic
native speakers; researchers estimate that this number
ranges between 280 and 400 million Arabic native
speakers. Therefore, it is the fifth most used language
in the world, and one of the official languages of the
United Nations (UN) since 1974. The MSA is a
descendant of the Classical Arabic (CA) language (the
language of the holy Qur'an) that was used in the 6th
century [1-5]. MSA and CA are different mainly in
style and vocabulary.
The Arabic language used in the Arab world is
divided into two main versions: Modern Standard
Arabic (MSA) and Colloquial (dialectal) Arabic. The
MSA has no variants while Colloquial Arabic has
many regional variants (dialects). MSA is used mainly
in formal speeches and interviews, formal print media,
education, media, official correspondence, etc.
Colloquial Arabic represents the real native spoken
language used to communicate at homes, markets,
offices, etc. Before the Internet era, Colloquial Arabic
was known mainly in the spoken form not written
form [3]. Arabic has a wide number of dialects that
vary greatly between different Arab countries, cities,
towns, and villages. Arabic speech divides into two
main types: Bedouin and sedentary, and all of these
dialects are sedentary dialects. The variations
(vernaculars) in Arabic dialects are positively
proportional to their geographical distances.
The Web 2.0 era offers to its users around the globe
the ability to generate content (user-generated content
The International Arab Conference on Information Technology (ACIT’2015)
(UGC)). Web blogs and microblogs (Facebook,
Twitter, Google +, etc.) have a huge amount of UGC
that represent an important source of invaluable
information about the users’ needs, trends, opinions,
etc. Extracting and analyzing such information
manually is not an option. Therefore, such huge amount
of UGC needs efficient and effective algorithms to be
implemented. Different languages and dialects are used
to generate this huge amount of UGC. Online social
networking services like Facebook, Instagram, Twitter,
YouTube, Google+, Vine, LinkedIn, Yahoo!, Pinterest,
and Tumblr are used to collect the necessary datasets to
conduct sentiment analysis and opinion mining.
This paper aims to lay a cornerstone for the creation
of standard Arabic corpus for sentiment analysis and
opinion mining. Furthermore, this corpus is suitable to
be used for Arabic text classification studies. Such
corpus can be enlarged or contracted according to the
requirements of different researchers. The Maktoob
Yahoo! website is used to collect 250 topics distributed
equally among five selected topics (Economy, FoodLife style, Religion, Sport, and Technology); however,
the numbers of collected reviews for each topic are not
equal. The total number of collected reviews is 1,442
written by 865 unique IDs (including 63 reviews
written by users with no IDs). The numbers of collected
reviews for each domain are (arranged from largest to
smallest): Sport (465 reviews), Religion (378 reviews),
Economy (222 reviews), Food-Life style (222 reviews),
and Technology (155 reviews). The average number of
collected reviews per topic is 5.76. Our initial
investigation on the collected Arabic reviews from the
Maktoob Yahoo! website shows that around 64%, 19%,
6%, and 3% of these reviews are written in MSA,
Egyptian dialect, Levantine dialect, and English
respectively. Furthermore, some topics are related to
more than one domain. Therefore, there are 7
subclasses: Arts, Economy, Education, Food, Politics,
Religion, and Sport. This corpus can be used for studies
that include supervised learning, as well as those that
include creating sentiment lexicons for sentiment
analysis studies. Corpora are created either
automatically or manually. We prefer to use the manual
approach to ensure that we get the highest possible
quality. The creation of this corpus consumes a lot of
time to collect and annotate different topics and
reviews. The main contribution of this study is creating
a multi-domain and multi-dialect Arabic dataset. The
annotation is manually conducted to guarantee the best
results. This corpus can be downloaded from
https://drive.google.com/file/d/0B1847AXYiV_geFVne
VRVdG9fQVk/view?usp=sharing.
The remainder of this paper is organized as follow.
The next section (Section 2) exhibits related works to
the creation of corpora. Section 3 presents our proposed
standard Arabic sentiment analysis corpus with a
highlight on the merits and deficiencies of this corpus.
Section 4 shows a preliminary discussion of the
collected corpus and the results of the analysis
performed on it. Section 5 presents concluding
remarks about this paper and discussion of future
plans to enlarge this corpus.
2. Related Work
As mentioned in the previous section, sentiment
corpora are created either automatically or manually.
Our proposed corpus is created manually to guarantee
the highest possible quality. This section presents
some of the studies that are closely related to this one.
Sarmento et al. designed a rule-based system
supported by a sentiment lexicon to automatically
build a corpus for sentiment analysis. They focused on
comments posted on an online newspaper about
political entities. The experiments they conducted
revealed that negative comments are relatively easier
to recognize than positive ones due to irony and
polarity inversion and shifting [6]. Such challenges
motivated researchers to build a number of specialized
corpora. For example, Bosco et al. [7] constructed a
corpus to deal with irony in Italian. Zhang et al. [8]
constructed a corpus to deal with polarity shifting in
English.
Pak and Paroubek showed in their study how to
collect a corpus for emotion analysis from Twitter.
They collected a corpus of 300,000 tweets in English
evenly distributed among three classes: the class of
positive emotions such as happiness and joy, the class
of negative emotions such as sadness and anger and
the class of objective text expressing no opinion. The
authors performed linguistic analysis of the
constructed corpus and made some interesting remarks
such as the one about the strong emotional indication
of some Part-of-Speech (POS) tags [9].
In a very impressive work, Ptaszynski et al. took
the largest corpus of Japanese blogs consisting of five
billion words and designed a system to automatically
annotate it for affect analysis. In addition to dealing
with a massively sized corpus, the proposed system
worked on both word-level and sentence-level, dealt
with subjectivity and considered an extended set of
emotion classes (not just positive/negative) for the
purpose of affect analysis [10]. Another work on the
Japanese language is that of Shiramatsu et al. [11].
The authors developed Social Opinions and Concerns
for Ideal Argumentation (SOCIA) for the purpose of
concern assessment in Japanese regional communities.
To facilitate public involvement in such a task, the
authors developed O2 that based on Linked Open Data
The International Arab Conference on Information Technology (ACIT’2015)
(LOD) to facilitate Japanese public involvement in
regional communities. O2 is an e-Participation web
platform.
In addition to building and annotating corpora, the
field of sentiment analysis benefits from building and
annotating lexicons as lexicon based (unsupervised)
and semi-supervised approaches to sentiment analysis
are among the most studied approaches. Baccianella et
al. [12] presented the third version of the SentiWordNet
lexicon, which was constructed by annotating the
synsets of the famous WordNet lexicon according to
the sentiment they convey.
Rushdi-Saleh et al. [13, 14] presented in their study
an Opinion Corpus for Arabic (OCA) composed of only
500 movie reviews written in Arabic. This work is
considered one of the earliest works on Arabic
sentiment analysis corpora. The polarities of these 500
Arabic reviews are distributed equally among the
positive and negative classes. Therefore, their corpus
consists of 250 positive Arabic reviews and 250
negative Arabic reviews. The corpus was constructed
manually and then translated to English for comparison
purposes. While the effort to construct the OCA corpus
is considered pioneering due to the limited resources
for Arabic sentiment analysis, it does suffer from
certain drawbacks. It is relatively small in size, lacks
any neutral or objective reviews, and is restricted in a
domain, which motivated other researchers to build
their own corpora.
AWATIF is a sentence level multi-genre corpus of
(MSA) labelled for subjectivity and sentiment analysis
(SSA) is presented by Abdul-Mageed and Diab [15].
They used two types of annotation guidelines: simple
(SIMP) and linguistically-motivated and genre-nuanced
(LG), to find out how annotators would accomplish the
labelling of MSA sentences under three different
conditions. The total number of MSA sentences 10,729
collected from three sources: 2,855 MSA sentences
collected from multi-domain collection of news wire
stories called ATB1V3, 5,342 MSA sentences collected
from 30 Wikipedia Talk Pages (WTP), and 2,532
threaded conversations collected from seven Web
forums (WFs). Our corpus is characterized by the
inclusion of MSA and various Arabic dialects while
AWATIF is limited to MSA.
One of the earliest works approaching Arabic
sentiment analysis using an automatically constructed
sentiment lexicon was that of Al-Ayyoub, BaniEssa and
Alsmadi [16]. To test their approach, the authors
collected a small dataset of 900 tweets evenly
distributed among the positive, negative and neutral
classes. Their results contradict that of [6] in the sense
that their tool was more efficient in detecting positive
tweets than it was with negative and neutral ones.
In his Master’s thesis, Abdulla [17], manually
collected two datasets: a large one consisting of more
than 10,000 reviews and relatively smaller one
consisting of 2,000 reviews. Earlier versions of these
datasets were used in [18-22]. The smaller dataset
consisted of tweets written mainly in MSA and
Jordanian dialect. These tweets are distributed equally
among the positive and negative classes. On the other
hand, the larger dataset consisted of other dialects and
languages as well as other classes such as negative and
spam. This dataset was later used to automatically
construct a sentiment lexicon for the Arabic language.
Different Arabic Sentiment Analysis studies use
datasets and corpora of different sizes, and in most
cases these are constructed by the researchers who
conduct these studies. Khasawneh et al. [23]
constructed a small dataset that consists of only 1,000
Arabic reviews collected from two social media
websites (Facebook and Twitter). Two studies
conducted by Al-Kabi et al. [24, 25] constructed a
dataset that includes 1,080 Arabic reviews, and these
reviews use MSA and colloquial Arabic. In a relevant
work, Al-Kabi et al. [26] constructed a dataset consists
of 4,050 Arabic, Emoticons, and English reviews. So
it is larger and more diverse than the previous dataset.
Aly and Atiya presented, in their study [27], a
dataset called Large-scale Arabic Book Review
(LABR). The authors claimed that it is the largest
Arabic sentiment analysis dataset, since it consists of
over 63,257 Arabic reviews about 2,131 Arabic books.
The reviews were collected from a website that uses a
5-star rating system. Each review provides such a
rating which makes the automatic collection of the
dataset a feasible task. Similar to the other works on
Arabic corpora, this dataset lacks diversity in terms of
the Arabic dialects included. Being a publicly
available tool, it was easy for other researcher such as
[28] to perform a more extensive set of experiments
on the LABR dataset in order to improve the best
known accuracy for it.
Another important work based on the LABR
dataset was that of AL-Smadi et al. [29] in which the
authors selected 1,513 reviews out of the LABR
dataset and annotated them for aspect-based sentiment
analysis (ABSA). The annotation of this dataset
(called HAAD) was performed according to the
SemEval2014 Task4 guidelines.1 The authors
provided baseline experiments on the resulting dataset.
In a follow-up work, Obaidat et al. [30] showed how
to get a higher accuracy than the baseline experiments
of [29] using lexicon-based approaches. Finally, a
recent paper [31] exploited ABSA in order to evaluate
Arabic news affect on readers taking the Israel-Gaza
1
http://alt.qcri.org/semeval2014/task4/
The International Arab Conference on Information Technology (ACIT’2015)
conflict of 2014 as a case study. The authors collected a
large number of Facebook posts and comments, but
ended up annotating only 2,265 posts due to the nuance
difficulties in the ABSA annotation of such a tricky
case study.
3. Standard
Corpus
Arabic
Sentiment
Analysis
This section is dedicated to present the merits and
deficiencies of the collected corpus besides showing its
contents and structure. The collected corpus is stored
inside MS Access database that has five tables, where
each table is dedicated to one of the five domains
(classes): Economy, Food-Life style, Religion, Sport,
and Technology. The researchers identified 62
spam/irrelevant reviews, for which they used the code
IRR within the polarity field. Most of the spam reviews
are ads trying to attract people to some businesses, and
there are some spam reviews that try to provoke people
to revolute against their rulers, for example, in the
sports domain. We notice a good portion of users
accuse the Maktoob Yahoo! website of helping to
provoke conflicts in the Arab World.
The structure of the database tables used is shown in
Figure 1. Figure 1 shows the design view of each of the
five tables used to store this corpus. Each table has 16
columns (fields) as shown in Figure 1.
and Dialect) are used to store the number of likes,
number of dislikes, polarity values, the expected or
published gender of the reviewer, date of the
collection of each review, domain of each topic, Sub
domain of each topic, and the dialect of each review,
respectively.
Table 1. Summary of the Size and Content of the Corpus
Domain Name
Economy
Number
of
Arabic Topics
50
222
Avg. No.
of
Reviews per Topic
4.44
Number
of Reviews
Food-Life Style
50
222
4.44
Religion
50
378
7.56
Sport
50
465
9.3
Technology
50
155
3.1
Total
250
1442
5.76
The size and the content of the constructed corpus
are shown in Table 1. Table 1 shows clearly that the
250 collected topics are distributed equally on the five
domains (Economy, Food-Life style, Religion, Sport,
and Technology). The authors have no control on the
number of reviews about each of these topics. The
average number of opinions per topic reflects the
interest of people in that domain, so we deduce the
sport domain is the most interested domain among the
five selected domains, followed by religious topics,
followed by two domains that have same average
Economy and Food-Life style. Last and not least
interested domain is the Technology domain.
4. Experiments and Results
In this section, a number of preliminary analysis
and the results are shown. Table 2 shows the
percentages of dialects used to express the reviews
and comments about a different aspect of life in the
Arab world.
Table 2 and Table 3 show preliminary analysis to
the Arabic opinion mining corpus under consideration.
These analyses are related to the topics published by
the Maktoob Yahoo! website and use MSA to be
understandable by all Arabs. Therefore, these two
tables exhibit results about different authors within the
5 domains under consideration.
Table 2. Summary of the Size and Content of the Corpus
Figure 1. Structure of Corpus's tables
The ID of each review is identified within (Auto-ID)
as shown in Figure 1, and each collected topic has an id
(Topic-ID). The URL of each topic and reviews are
stored within the (URL) field. Furthermore, there are
columns for topic titles, topic content, reviews and
reviewer IDs. The other columns (Like-No, Dislike-No,
Polarity, Gender, Date-of-Collection, Class, Sub-Class,
Domain
Name
Economy
No. of
Characters
(no spaces)
2172
No. of
Characters
(with spaces)
2609
438
Avg.
Words per
Title
8.76
No. of
Words
Food-Life
Style
Religion
1507
1807
300
6.00
33375
3986
611
12.22
Sport
2217
2660
443
8.86
Technology
2104
2538
434
8.68
The International Arab Conference on Information Technology (ACIT’2015)
Total
41375
13600
2226
8.9
Table 2 shows the sizes of the titles of different
published articles measured in characters and words.
The shortest titles are used within Food-Life Style
domain and longest titles are used within religion
domain. The full list of the five domains sorted by
ascending size is: Food-Life Style, Technology,
Economy, Sport, and Religion. The average number of
words per title (last column) in table 2 is computed by
dividing each number in the number of words column
by 50 since we have 50 topics in each domain.
Table 3. Summary of the Size of the Topic contents
Domain
Name
Economy
No. of
Characters
(no spaces)
100390
No. of
Characters
(with spaces)
120611
20218
Avg.
Words per
Topic
404.36
No. of
Words
Food-Life
Style
Religion
31666
38170
6502
130.04
96769
116815
20045
400.9
Sport
35389
42435
7045
140.9
Technology
46948
56254
9305
186.1
Total
311162
374285
63115
252.46
technology domain and longest articles are used
within an economy domain. The full list of the five
domains sorted by ascending size of opinion's contents
is: Technology, Food-Life Style, Sport, Religion, and
Economy. Hence we can deduce that social media
users in the Arab world who interested in economy
likes to write more than others who are interested in
other domains, followed by users who are interested to
write their opinions about religious articles. Table 1
and Table 4 show that Technology domain has the
lowest interest by social media users in the Arab
world. The average number of review's words (last
column) in table 4 is computed by dividing each
number in the number of words column by the
corresponding number of opinions presented in table
1. Moreover, table 4 exhibits that average length of the
whole collection which is equal to 28.7. The
computations of the review lengths include spam
reviews.
Table 5 is based on the 1442 reviews collected and
shown in Table 1. The dialect of only 146 reviews can
not be identified. Therefore, Table 5 is based on 1296
reviews only.
Table 5. Summary of Corpus's Languages
Table 3 shows the size of the topic contents of
different published articles measured in characters and
words. The shortest articles are used within Food-Life
Style domain and longest articles are used within an
economy domain. The full list of the five domains
sorted by ascending size of article's contents is: FoodLife Style, Sport, Technology, Religion, and Economy.
The two sorted lists of domains by the sizes of article's
titles and article's contents are different. Hence, there is
no relation between the sizes of the titles and the sizes
of article's contents. The average number of words per
topic (last column) in table 3 is computed by dividing
each number in the number of words column by 50
since we have 50 topics in each domain.
Table 4. Summary of the Size of the Review Contents
Domain
Name
Economy
No. of
Characters
(no spaces)
43158
No. of
Characters
(with spaces)
52695
No. of
Words
Avg. Words
per Opinion
9526
42.91
Food-Life
Style
Religion
20600
24994
4393
19.79
71031
86719
15687
41.50
Sport
53573
65144
11574
24.89
Technology
10362
12596
2234
14.41
Total
198724
242148
43414
28.7
Table 4 shows the size of the opinions about
different published articles measured in characters and
words. The shortest opinions are used within
Language Name
MSA
Egyptian
Levantine
English
Arabian Peninsula
Mesopotamian group
French
MSA + English
Arabizi
Maghrebi group
MSA + Emoticons
Egyptian + Emoticons
Percentage
848 / 1296 = 65.43%
241 / 1296 = 18.59%
74 / 1296=5.70%
40 / 1296 = 3.08%
32 / 1296 = 2.46%
32 / 1296 = 2.46%
10 / 1296 = 0.771%
7 / 1296 = 0.54%
5 / 1296 = 0.385%
4 / 1296 = 0.308%
2 / 1296 = 0.154%
1 / 1296 = 0.077%
Table 5 shows clearly the distribution of the
languages used by the users in the Arab world to
express their comments and reviews. It is clear that the
vast majority (64.081%) still use MSA since it is
supposed to be the most comprehendible version of
Arabic across the 27 countries in which it represents
the official language. Egypt, the most populated
country in the Arab world, reached a population of 94
million as announced by the country’s official state
information service [32]. So, it is no wonder that the
second language used in this part of the world is the
Egyptian dialect.
In this study, by Levantine, we mean Levantine
Arabic, the dialect spoken in Jordan, Syria, Lebanon
and Palestine. The third common dialect used is
Levantine (6.155%), since it is used mainly in Levant
region (Eastern Mediterranean) that includes four
countries (Syria (20 million), Jordan (8 million),
Lebanon (5 million), and Palestine (4.5 million) with
The International Arab Conference on Information Technology (ACIT’2015)
40 million residents and substantially large presence in
the media in the Arab world [33]. The three top
commonly used dialects in this region are followed by
English, Arabian Peninsula group, Mesopotamian
group, French, MSA + English, Arabizi, Maghrebi
group, MSA + Emoticons, and Egyptian + Emoticons,
respectively as shown in Table 5.
Table 6 shows the distribution of the polarities
among the five classes under consideration. The
process of the manual annotation of this modest corpus
consumes a lot of time, and in many cases we found
that no two human professionals can assign the same
polarities to a number of confusing reviews. Therefore,
we decide to conduct a study about the manual
annotation of Arabic reviews and comments. Not all
reviews in this corpus can be annotated by human as
positive, negative or neutral easily. Table 6 shows that
majority of reviews in the economy and religion
domains are negative, while the majority of reviews in
Food-Life Style, Sport, and Technology domains are
positive. The last row in table 6 shows the majority of
collected reviews in this corpus are negative and the
overall percentage of irregular and spam reviews is
4.3%.
Sport
Technology
Total
Average
Un
Known
Religion
No. of
Irregular/Sp
am
Polarities
Food-Life
Style
No. of
Neutral
Polarities
Economy
No. of
Negative
Polarities
Domain
Name
No. of
Positive
Polarities
Table 6. Summary of Polarity Distribution among the 5 Domains
33
(14.86%)
84
(37.83%)
87
(23.01%)
216
(46.45%)
58
(37.41%)
130
(58.55%)
68
(30.63%)
247
(65.34%)
149
(32.04%)
36
(23.22%)
27
(12.16%)
31
(13.96%)
38
(10.05%)
23
(4.94%)
26
(16.77%)
32
(14.41%)
39
(17.56%)
6
(1.58%)
25
(5.37%)
25
(16.12%)
0
(0%)
0
(0%)
0
(0%)
52
(11.18%)
10
(6.45%)
630
145
127
62
43.68%
10.05%
8.80%
4.3%
478
33.15%
The distribution of Internet users according to their
gender among the five domains is presented in table 7.
The authors of this study could not identify the gender
of 21% of all Maktoob Yahoo! who wrote the reviews
we collected in this study. Therefore, two percentages
are presented in table 7. The top percentages include
the unknown gender values while the bottom
percentages exclude the unknown gender values. Table
7 shows clearly that the males constitute the majority
within all domains, the highest percentage of females
was within Food-Life Style domain. The overall
percentages show that female Maktoob Yahoo! users
constitute one-fourth of male Maktoob Yahoo! users.
Table 7. Summary of Gender Distribution among the 5 Domains
Domain Name
Economy
Food-Life Style
Religion
Sport
Technology
Male
130
(59%)
(86%)
117
(53%)
(67%)
276
(73%)
(84%)
292
(63%)
(80%)
99
(64%)
(82%)
Female
21
(0.9%)
(14%)
57
(0.25%)
(33%)
52
(14%)
(16%)
73
(16%)
(20%)
22
(14%)
(18%)
Unknown
Gender
Total
71
(32%)
222
48
(22%)
222
50
(13%)
378
100
(21%)
465
34
(22%)
155
1442
Total
914
225
303
Average
63%
80%
16%
20%
21%
5. Conclusion and Future Work
This paper shows the creation of a flexible preliminary
Arabic corpus for sentiment analysis and opinion
mining for Arabic reviews and comments. This corpus
characterizes by its flexibility, where each of its users
can add, delete or revise it. Arabic comments and
reviews within this corpus constitute mainly of Arabic
comments (MSA and Colloquial (dialectal) Arabic).
Our analysis shows that most of the users of Maktoob
Yahoo! prefer to use MSA (65.43%), to enable
different Arab visitors to comprehend their comments.
Additionally this corpus has few comments and
reviews that used English, French, Emoticons, etc.
This corpus consists of 250 topics equally divided
among the five classes (domains) we choose to
include in this corpus. We plan to expand this corpus
by adding more classes, and adding a new domain for
political topics and reviews, and another one for Arts
and stars. Moreover, we plan on adding more topics
and reviews to each of the five domains within this
corpus. Our future plan includes more detailed
analysis of the collected topics and reviews, besides
releasing the new corpus to be used freely by different
researchers in the field of sentiment analysis, text
mining, and data mining.
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