Analysing Sentiment in Arabic Posts
In the age of digital communication, social media has become a window into public opinion, emotion, and cultural trends—especially in the Arabic-speaking world. While automated tools like sentiment analysis models exist, manual sentiment analysis remains a powerful method, especially when dealing with complex, nuanced, or dialect-heavy content. This article walks you through the process of manually analyzing sentiment from Arabic social media posts, step by step.
1. Understanding Sentiment Analysis
Sentiment analysis is the process of identifying the emotional tone behind a piece of text. Typically, this involves categorizing content into one of three categories:
- Positive (e.g., praise, satisfaction, happiness)
- Negative (e.g., criticism, anger, sadness)
- Neutral (e.g., factual statements without emotional charge)
When done manually, the goal is to read and interpret each post carefully, considering not just the words used but also context, tone, and cultural references.
2. Challenges Specific to Arabic
Before diving in, it’s important to acknowledge why manual sentiment analysis is especially useful for Arabic content:
- Dialect Variation: Arabic has dozens of dialects, and users often write in colloquial Arabic rather than Modern Standard Arabic (MSA).
- Code-Switching: Many users mix Arabic with English, French, or “Arabizi” (Arabic written in Latin letters).
- Informal Writing: Social media posts are full of slang, abbreviations, emojis, and non-standard spelling.
Automated tools often struggle with these features, which is where human interpretation shines.
3. Steps for Manual Sentiment Analysis
Step 1: Collect the Posts
Step 2: Read the Post Carefully
Step 3: Identify Emotionally Charged Words
Keywords like: Positive: جميل (beautiful), رائع (wonderful), أحب (I love), ممتاز (excellent)