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Identifying Mubasher software products through sentiment analysis of Arabic tweets

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

Social media has recently become a rich resource in mining user sentiments. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. Firstly, document's Pre-processing are explored on the dataset. Secondly, Naive Bayes and Support Vector Machines (SVMs) are applied with different feature selection schemes like TF-IDF (Term Frequency-Inverse Document Frequency) and BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment analysis is expanded to obtain the results for N-Grams term of tokens. Finally, human has labelled the data and this may involve some mistakes in the labelling process. At this moment, neutral class with generalisation of our classification will take results to different classification accuracy.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 02/05/2016

Publication status

Published - 02/05/2016

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781467387439

External Publication IDs

  • handle.net: 10547/623853
  • Scopus: 84969523527

Host publication title

2016 International Conference on Industrial Informatics and Computer Systems (CIICS)

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