Arabic natural language processing (NLP) is an advanced application of AI and machine learning used to understand Arabic dialects. Our focus in this article is on applying NLP in Arabic to extract semantic insights from all sources of data, be it text, audio, or video. We talk about how, at Repustate, we use NLP for sentiment analysis. We also showcase a real-world example of how we successfully provided a highly customized Arabic sentiment analysis solution for a client in Saudi Arabia.

2. A Guide to Arabic Natural
Language Processing

3. Table of Content
● Challenges in Arabic natural language processing
● How is Arabic NLP used for sentiment analysis?
● What is the process of doing sentiment analysis in Arabic
tweets?

4. Challenges in Arabic natural language
processing
Arabic is a very complex language, with many dialects. Arabic morphology
has two fundamentals: derivational morphology, (word formation) and
inflectional morphology (how words interact with the syntax).
Morphological features have integrated dependencies on several linguistic
factors like affixes, root-based structures, and vowels. Additionally, Arabic’s
high ability for new word formations with rich semantic meanings - all pose
quite a challenge for Arabic natural language processing.

5. How is Arabic NLP used for sentiment
analysis?
For accurate arabic sentiment analysis, the sentiment mining tool needs to
read the data directly in Arabic. For this, it needs to have an algorithm that
has been trained on Arabic datasets (to hone the machine learning model)
using Arabic NLP. If the tool resorts to translating text to English or any
other language first, it will give wrong sentiment scores, translating to a
company investing in business and operational strategies based on
incorrect insights. Repustate’s Arabic sentiment analysis solution reads and
understands the Arabic language in its native format. It supports standard
Arabic and can be trained on three Arabic dialects - Gulf Peninsular,
Levantine, and Egyptian. Our platform has its own Arabic lemmatizer, part-
of-speech tagger, and sentiment models. This is how it conducts Arabic NLP
tasks to understand the intent behind texts and return a corresponding
classification and sentiment score.

6. What is the process of doing sentiment
analysis in Arabic tweets?

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