LEBANONUPRISING: a thorough study of Lebanese tweets
Reda Khalaf, Mireille Makary

TL;DR
This paper analyzes Lebanese tweets related to the October 2019 uprising, developing sentiment and emotion detection methods, creating a Lebanese-Arabic dictionary, and comparing datasets over time.
Contribution
It introduces a new Lebanese Arabic sentiment analysis framework, a Lebanese-Standard Arabic dictionary, and explores emotion detection using emojis in social media.
Findings
Sentiment analysis achieved satisfactory accuracy.
Emotion detection identified sarcastic and funny tweets.
Variation observed between datasets from different periods.
Abstract
Recent studies showed a huge interest in social networks sentiment analysis. Twitter, which is a microblogging service, can be a great source of information on how the users feel about a certain topic, or what their opinion is regarding a social, economic and even political matter. On October 17, Lebanon witnessed the start of a revolution; the LebanonUprising hashtag became viral on Twitter. A dataset consisting of a 100,0000 tweets was collected between 18 and 21 October. In this paper, we conducted a sentiment analysis study for the tweets in spoken Lebanese Arabic related to the LebanonUprising hashtag using different machine learning algorithms. The dataset was manually annotated to measure the precision and recall metrics and to compare between the different algorithms. Furthermore, the work completed in this paper provides two more contributions. The first is related to building…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Spam and Phishing Detection · Advanced Text Analysis Techniques
