NLP , AI

1. ARABIC SENTIMENT ANALYSIS
O F F O O D DEL IVERY SERVICES
REVIEWS
DR. DHEYA MUSTAFA
FACULTY OF ENGINEERING
THE HASHEMITE UNIVERSITY
Oct 24, 2023 ISNCC’2023, Doha, Qatar 1

2. OUTLINE
•Introduction
•Motivation
•Goal
•Contribution
•Proposed Model
•Performance Evaluation
•Conclusion
2

3. INTRODUCTION
• Food delivery services (FDSs) have
introduced diversity to the increasing
demand for online food delivery
marketplaces.
• Mostly third-party marketplace apps for
global ordering and delivery, such as
UberEATS, Talabat (the Arabic word for
orders), and Menulog.
• Employ a cost intensive, aggregator
business model and are in charge of all
delivery logistics. 3

4. MOTIVATION
• The majority of businesses want to
effectively increase customer
satisfaction by using data to identify
areas for improvement.
–Mostly based on reviews from
customers.
• Sentiment analysis (SA) can
determine the customers’ opinion
based on the written content.
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5. GOAL
• Apply SA to Arabic content to give an even
better service, driven by the feedback of
customers.
• Offer a quick and efficient monitoring
system to handle the needs clients on a
large scale at a low cost and maximum
profit.
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6. METHODOLOGY
• gathering Arabic reviews regarding FDS and
evaluating feelings using popular ML methods.
• Created an Arabic dialects lexicon including
various dialects and their standard orientation
in Arabic.
• Emotions are identified and analyzed using an
existing emotion lexicon.
• experiments were carried out before and
after applying cleaning and preprocessing .
• Negative sentiments were analyzed to identify
the possible causes. 6

7. 7

8. DATASET CLEANING
• The raw datasets contain many empty and repetitive reviews that
should be efficiently eliminated.
• Involves removing duplications, identifying emojis, characters,
and word replacements, spelling correction, and annotation.
• SA of dialectical Arabic is challenging.
• We constructed a dialects dictionary manually to translate
dialectical phrases into equivalent MSA in the context of FDS.
• Popular English words written in the Arabic alphabet in the
reviews are included in the dialects dictionary as well.
– Example, the word “nice” is written as ‘‫نايس‬ ’ and is equivalent
to the word ‘‫جميل‬ ’ in Arabic.
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9. 9

10. ARABIC REVIEWS
PREPROCESSING
• Clean review texts are process using common NLP
tasks.
• normalization : represent step characters that have
more than one form like ( ،‫أ‬
،‫إ‬
‫آ‬ ) in a unified form.
• Also removed any punctuations, diacritics, and tatweel.
• Stopword removal: reduce the size of the text by
removing unnecessary particles and pronouns.
• Tokenization: split the text into single units such as
sentences and then into single words.
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11. ARABIC REVIEWS
PREPROCESSING
• Part of Speech (PoS) tagging: annotate words in a text
based on their type and their relationships to
neighboring and related words in that text.
– Based on the Stanford tagger [35].
• The last NLP task involved removing suffixes or
prefixes.
– To efficiently convert each word in its base form without
extensions (Stemming)
– Used the tashaphyne stemmer
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13. SENTIMENT
CLASSIFICATION
• The final phase was to build and train the machine
learning (ML) classifier using a labeled subset of
the dataset (Talabat FDS ).
• We tested the model on unseen reviews and
categorized them into positive, negative, or
neutral classes.
• ML models: DT and SVM
– Most frequently used classifiers in Arabic SA
– Often provide the best results over other ML classifiers

14. EVALUATION
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15. EVALUATION
15
Experiment
DT classifier SVM Classifier
P% R% F% Acc% P% R% F% Acc%
Before
cleaning
and pre-
processin
g
+ve 67 95 79 87 86 86
-ve 82 44 57 80 86 83
Neu 20 4 6 31 23 26
Avg 68 69 65 69 79 80 80 80
After
cleaning
and pre-
processin
g
+ve 72 91 81 87 88 88
-ve 76 60 67 77 89 83
Neu 20 3 5 33 10 15
Avg 69 73 69 73 79 82 80 82
+ve: positive -ve: negative Neu: neutral Avg:
average

16. EVALUATION
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17. EVALUATION
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Fig. 3. A confusion matrix of the SVM classifier after applying cleaning
and preprocessing phases.

18. EVALUATION
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19. CONCLUSION
• Proposed a sentiment analysis model of Arabic dialect
reviews about FDS.
• applied intensive preprocessing and several aspects of
analyzing Arabic text to generate a dataset.
• examined two well-known ML classifiers over this
dataset and provided a comparative performance
evaluation
• Analyzed the most representative negative reviews
and produced a word cloud.
– The long waiting time, cold food, wrong order, and
bill were the most important factors contributing
to the negative sentiments.
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20. Thank you
Oct. 23, 2023 ISNCC'2014, Doha, Qatar 20