Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Emotion analysis and Opinion mining from text play an important role in many fields nowadays. Their importance comes from many perspectives; Governments need to know the impact of some economic and political decisions, companies need to get the feedback on their new products, other people need to know others opinions before buying any new thing. These opinions besides the emotions incorporated in blogs, reviews on products and social media represent a valuable resource of information for decision makers. The aim of the thesis is to propose an emotion approach and suggest a framework for classifying the text based on its emotions such as (anger, disgust, fear, joy, sadness and surprise). These emotions come under two main opinion classes; they are (positive and negative). To accomplish this work, first, a comparison is performed between different emotion analysis techniques to classify the strengths and weaknesses which formulate the research objectives. Two main objectives have been addressed first, improving emotion classification accuracy by using a hybrid approach by using Information Retrieval, Machine Learning and Lexicon based techniques

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