Publications

2016 A Meta-Framework for Modeling the Human Reading Processin Sentiment Analysis

Journal Article

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach.HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.

  • BibTex Key
  • Authors Ahmad Al-Sallab | Hazem Hajj | Khaled Bashir Shaban | Ramy Baly | Roula Hobeica | Wassim El-Hajj
  • Tags human reading | psychology | Sentiment Analysis | supervised learningand notions
  • DOI Number 10.1145/2950050
  • Publisher ACM Transactions on Information Systems

    2018 A Survey of Opinion Mining in Arabic: A Comprehensive System Perspective Covering Challenges and Advances in Tools, Resources, Models, Applications and Visualizations

    Journal Article

    Opinion mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language which is the 5thmostspoken language, and has become the 4thmost used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for more up-to-date literature survey. The aim of this paper is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this paper provides a comprehensive system perspective by covering advances in different aspects of an opinion mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The paper provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion mining system. Key insights are captured at the end of each section for particular aspects of the opinion mining system giving the reader a choice of focusing on particular aspects of interest.

    • BibTex Key
    • Authors Ahmad Al-Sallab | Ali Hamdi | Gilbert Badaro | Hazem Hajj | Khaled Bashir Shaban | Nizar Habash | Ramy Baly | Wassim El-Hajj
    • Tags Arabic Natural Language Processing | Deep Learning | Opinion Mining | Sentiment Analysis | Sentiment Analysis Applications | Sentiment Lexicons