Analysis of the relationship between Saudi twitter posts and the Saudi stock market

Sentiment analysis has become the heart of social media research and many studies have been applied to obtain users' opinion in fields such as electronic commerce and trade, management and also regarding political figures. Social media has recently become a rich resource in mining user sentiments. Social opinion has been analysed using sentiment analysis and some studies show that sentiment analysis of news, documents, quarterly reports, and blogs can be used as part of trading strategies. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with the Saudi stock market in order to carry out and illustrate the relationship between Saudi tweets (that is standard and Arabian Gulf dialects) and the Saudi market index. To the best of our knowledge, this is the first study performed on Saudi tweets and the Saudi stock market.

[1]  Vincent Martin Predicting the French Stock Market Using Social Media Analysis , 2013, SMAP.

[2]  Steven Skiena,et al.  Trading Strategies to Exploit Blog and News Sentiment , 2010, ICWSM.

[3]  Khurshid Ahmad,et al.  Visualising sentiments in financial texts? , 2005, Ninth International Conference on Information Visualisation (IV'05).

[4]  Zeynab Abbasi Khalifelu,et al.  Analysis and evaluation of unstructured data: text mining versus natural language processing , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[5]  Vincent Martin,et al.  Predicting the French Stock Market Using Social Media Analysis , 2013, 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization.

[6]  Sunil Kumar Khatri,et al.  Sentiment analysis to predict Bombay stock exchange using artificial neural network , 2014, Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization.

[7]  Abdulmohsen Al-Thubaity,et al.  Automatic Arabic Text Classification , 2008 .

[8]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[9]  S. Ozdemir,et al.  Analysis of the relation between Turkish twitter messages and stock market index , 2012, 2012 6th International Conference on Application of Information and Communication Technologies (AICT).

[10]  Philipp Koehn,et al.  Synthesis Lectures on Human Language Technologies , 2016 .

[11]  Li Zhou,et al.  Sentiment classification for stock news , 2010, 5th International Conference on Pervasive Computing and Applications.

[12]  Xin Wang,et al.  Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets , 2010, COLING.

[13]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[14]  Mohamed M. Mostafa,et al.  More than words: Social networks' text mining for consumer brand sentiments , 2013, Expert Syst. Appl..

[15]  Amine Bensaid,et al.  Automatic Arabic Document Categorization Based on the Naïve Bayes Algorithm , 2004 .

[16]  Alaa M. El-Halees,et al.  Arabic Text Classification Using Maximum Entropy , 2015 .

[17]  Estevam R. Hruschka,et al.  Tweet sentiment analysis with classifier ensembles , 2014, Decis. Support Syst..

[18]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[19]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[20]  Khairullah Khan,et al.  Mining opinion components from unstructured reviews: A review , 2014, J. King Saud Univ. Comput. Inf. Sci..