مجال
التميز
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تميز دراسي و بحثي
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البحوث المنشورة
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البحث (1):
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عنوان البحث:
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THE IMPORTANCE OF NEUTRAL CLASS IN SENTIMENT
ANALYSIS OF ARABIC TWEETS
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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02/04/2016
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موجز عن البحث:
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Product reviews are becoming increasingly useful. In
this paper, Twitter has been chosen as a platform for opinion mining in
trading strategy with Mubasher products, which is a leading stock analysis
software provider in the Gulf region. This experiment proposes a model for
sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets
to extract feedback from Mubasher products. A hybrid of natural language
processing and machine learning approaches on building models are used to
classify tweets according to their sentiment polarity into one of the classes
positive, negative and neutral. In addition, regarding to the comparison
between SVM and Bayesian method, we have split the data into two independents
subsets form different periods and the experiments were carried out for each
subset respectively in order to distinction between positive and negative
examples by using neutral training examples in learning facilitates. Similar
result has been given.
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البحث (2):
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عنوان البحث:
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Visualising
Arabic Sentiments and Association Rules in Financial Text
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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01/03/2017
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موجز عن البحث:
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Text mining methods involve various
techniques, such as text categorization, summarisation, information
retrieval, document clustering, topic detection, and concept extraction. In
addition, because of the difficulties involved in text mining, visualisation
techniques can play a paramount role in the analysis and pre-processing of
textual data. This paper will present two novel frameworks for the
classification and extraction of the association rules and the visualisation
of financial Arabic text in order to realize both the general structure and
the sentiment within an accumulated corpus. However, mining unstructured data
with natural language processing (NLP) and machine learning techniques can be
arduous, especially where the Arabic language is concerned, because of
limited research in this area. The results show that our frameworks can
readily classify Arabic tweets. Furthermore, they can handle many antecedent
text association rules for the positive class and the negative class.
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البحث (3):
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عنوان البحث:
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Sentiment
Analysis of Arabic Tweets in e-Learning
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رابط إلى البحث:
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Click Here
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تاريخ النشر:
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18/02/2017
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موجز عن البحث:
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In this study, we present the design
and implementation of Arabic text classification regarding university
students’ opinions through different algorithms such as Support Vector
Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a
framework to analyse Twitter “tweets” as having negative, positive or neutral
sentiments in education or, in other words, to illustrate the relationship
between the sentiments conveyed in Arabic tweets and the students’ learning
experiences at universities. Two experiments were carried out, one using
negative and positive classes only and the other one with a neutral class.
The results show that in Arabic, a sentiments SVM with an n-gram feature
achieved higher accuracy than NB both with using negative and positive
classes only and with the neutral class.
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البحث (4):
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عنوان البحث:
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Clustering Students‘ Arabic Tweets using
Different Schemes
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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2017
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موجز عن البحث:
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In this paper, Twitter has been chosen
as a platform for clustering the topics that have been mentioned by King
Abdulaziz University students to understand students’ behaviours and answer
their inquiries. The aim of the study is to propose a model for clustering
analysis of Saudi Arabian (standard and Arabian Gulf dialect) tweets to
segment topics included in the students’ posts. A combination of the natural
language processing (NLP) and the machine learning (ML) method to build
models is used to cluster tweets according to their text similarity. K-mean
algorithm is utilised with different vector representation schemes such as
TF-IDF (term frequency-inverse document frequency) and BTO (binary-term
occurrence). Distinct preprocessing is explored to obtain the N-grams term of
tokens. The cluster distance performance task is applied to determine the
average between the centroid clusters. Moreover, human evaluation clustering
is performed by looking at the data source to make sure that the clusters are
making sense to an educational domain. At this moment, each cluster has been
identified, and students’ accounts on Twitter have been known by their
facilities or their educational system, such as e-learning. The results show
that the best vector’s representation was using BTO, and it will be useful to
apply it to cluster students’ text instead of the TF-IDF scheme.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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IEEE Seventh International Conference on Intelligent
Computing and Information Systems, ICICIS15
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تاريخ الإنعقاد:
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12/12/2015
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مكان
الإنعقاد:
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Cairo, Egypt
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طبيعة المشاركة:
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Paper
presentation
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عنوان المشاركة:
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Analysis of the
Relationship Between Saudi Twitter Posts and the Saudi Stock Market
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ملخص المشاركة:
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Sentiment analysis has become the heart of social
media research and many studies have been applied to obtain users’ opinion in
fields such as electronic commerce and trade, management and regarding
political figures. Social media has recently become a rich resource in mining
user sentiments. Social opinion has been analysed using sentiment analysis
and some studies show that sentiment analysis of news, documents, quarterly
reports, and blogs can be used as part of trading strategies. In this paper,
Twitter has been chosen as a platform for opinion mining in trading strategy
with the Saudi stock market in order to carry out and illustrate the
relationship between Saudi tweets (that is standard and Arabian Gulf
dialects) and the Saudi market index. To the best of our knowledge, this is
the first study performed on Saudi tweets and the Saudi stock market.
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المؤتمر (2):
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عنوان المؤتمر:
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2016
International Conference on Industrial Informatics and Computer Systems
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تاريخ الإنعقاد:
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13/03/2016
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مكان
الإنعقاد:
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Sharjah,
United Arab Emirates
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طبيعة المشاركة:
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Paper
presentation
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عنوان المشاركة:
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Identifying Mubasher Software Products through
Sentiment Analysis of Arabic Tweets
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ملخص المشاركة:
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Social media has recently become a rich
resource in mining user sentiments. In this paper, Twitter has been chosen as
a platform for opinion mining in trading strategy with Mubasher products,
which is a leading stock analysis software provider in the Gulf region. This
experiment proposes a model for sentiment analysis of Saudi Arabic (standard
and Arabian Gulf dialect) tweets to extract feedback from Mubasher products.
A hybrid of natural language processing and machine learning approaches on
building models are used to classify tweets according to their sentiment
polarity into one of the classes positive, negative and neutral. Firstly,
document’s Pre-processing are explored on the dataset. Secondly, Naive Bayes
and Support Vector Machines (SVMs) are applied with different feature
selection schemes like TF-IDF (Term Frequency–Inverse Document Frequency) and
BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment
analysis is expanded to obtain the results for N-Grams term of tokens.
Finally, human has labelled the data and this may involve some mistakes in
the labelling process. At this moment, neutral class with generalization of
our classification will take results to different classification accuracy.
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