Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis

TL;DR

This paper introduces a novel sentiment analysis model for Arabic social media text that combines lexical features, emoticons, and input text characteristics, achieving state-of-the-art accuracy across multiple dialects and datasets.

Contribution

The paper presents a new feature set and machine learning model that significantly improves Arabic sentiment analysis performance on diverse social media datasets.

Findings

Achieved higher accuracy on six out of seven datasets

Outperformed all existing publicly available Arabic sentiment analysis systems

Validated across multiple Arabic dialects and MSA datasets

Abstract

The importance of building sentiment analysis tools for Arabic social media has been recognized during the past couple of years, especially with the rapid increase in the number of Arabic social media users. One of the main difficulties in tackling this problem is that text within social media is mostly colloquial, with many dialects being used within social media platforms. In this paper, we present a set of features that were integrated with a machine learning based sentiment analysis model and applied on Egyptian, Saudi, Levantine, and MSA Arabic social media datasets. Many of the proposed features were derived through the use of an Arabic Sentiment Lexicon. The model also presents emoticon based features, as well as input text related features such as the number of segments within the text, the length of the text, whether the text ends with a question mark or not, etc. We show that the presented features have resulted in an increased accuracy across six of the seven datasets we've experimented with and which are all benchmarked. Since the developed model out-performs all existing Arabic sentiment analysis systems that have publicly available datasets, we can state that this model presents state-of-the-art in Arabic sentiment analysis.