Arabic tweets sentiment analysis – a hybrid scheme

Abstract

The fact that people freely express their opinions and ideas in no more than 140 characters makes Twitter one of the most prevalent social networking websites in the world. Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public’s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present – using Saudi Arabia as a basis – we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach that combines semantic orientation and machine learning techniques. Through this approach, the lexical-based classifier will label the training data, a time-consuming task often prepared manually. The output of the lexical classifier will be used as training data for the SVM machine learning classifier. The experiments show that our hybrid approach improved the F-measure of the lexical classifier by 5.76% while the accuracy jumped by 16.41%, achieving an overall F-measure and accuracy of 84 and 84.01% respectively.

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

“…In the proposed approach, the authors linked an Arabic morphological analyzer Aramorph lexicon with Senti-WordNet and included MSA only. For Saudi dialect, there are some proposed lexicons such as Adayel and Azmi [4] for Saudi dialect lexicon. This lexicon contains only around 1500 terms.…”

T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme

“…The proposed polarity update method improves on the approach presented by Aldayel and Azmi () by using switch and switch‐neutral methods for changing the polarity of a term used in tweets. The authors of Aldayel and Azmi () used a supervised learning technique for updating the polarity of words in reviews, whereas we compute the new sentiment score of a term using a revised scheme of (a) polarity switching, (b) neutral switching, and (c) term weighting. This enhancement contributes significantly to the accurate scoring of domain‐specific words, as reported in the results and evaluation section.…”

“…The recent studies (Aldayel & Azmi, 2016;Asghar, Ahmad, Qasim, Zahra, & Kundi, 2016;Duwairi & El-Orfali, 2014;Ikeda, Takamura, Ratinov, & Okumura, 2008) have demonstrated that their sentiment update methods produce promising results with respect to accurate scoring of domainspecific words in the sentiment analysis of user reviews. They achieved significant improvement in accuracy in both the hotel and movie domains.…”

Implementing ERP Systems in Government: Case Study of Saudi Organization

“…It provide several advantages such as improved process flow, reduced inventories, better data analysis, better customer service, better enterprise performance and higher efficiency. All of these attract organizations to adopt ERP systems to strongly enter the competitive market [5]. A successful ERP implementation needs a long time and high recourse to be acquired.…”