Future University In Egypt (FUE)
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Mahmoud Sami Abd El-Aziz Othman

Basic information

Name : Mahmoud Sami Abd El-Aziz Othman
Title: lecturer
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Personal Info: Mahmoud Sami is a lecturer at the Faculty of Computer and Information Technology, Future University. He graduated from Faculty of Computers and Information, Cairo University with grade Excellent; moreover, he was from the top ten students in computer science department. He majored in Computer Science and a minor in Information Systems. After two years from graduation, he got his master’s degree from the same faculty he graduated from. He received two awards as an outstanding TA for the academic years 2011-2012 and 2013-2014. He got his Ph.D. degree from the same faculty he graduated from. View More...

Education

Certificate Major University Year
PhD Computer Science Cairo University - Faculty of Computers & Information 2016
Masters Computer Science Cairo University - Faculty of Computers & Information 2012
Bachelor Computer Sciences Cairo University - Faculty of Computers & Information 2008

Teaching Experience

Name of Organization Position From Date To Date
Company 4S 01/08/2007 31/07/2008

Researches /Publications

Using NLP Approach for Opinion Types Classifier - 01/0

Mahmoud Sami Abdelaziz Othman

Hesham Hassan, Ramadan Moawad, Amira M. Idrees

01/09/2016

Information that are represented as text are either facts or opinions, whenever we need to make a decision, we often seek out the opinions of others which is one of the most influencing factors for our decisions. Traditionally, individuals can get opinions from friends and family while organizations use surveys, focus groups, opinion polls and consultants. Nowadays, opinions expressed through user generated content are considered as one of the important types of information which is available on the web, therefore, many resources have been emerged for expressing opinions including social media and others. This situation has revealed the necessity for robust, flexible Information Extraction (IE) systems, these systems have the availability to transform the web pages into program-friendly structures such as a relational database to reveal these opinions. In this paper, we propose an approach to classify the opinions of a document or a set of documents considering an object. The approach has been implemented and applied on a dataset of opinions. The proposed system discover the opinions provided for an object in a document or set of documents. The system discovers different types of opinionated statements, including the opinionated, comparative, superlative, and non- opinionated. The system has been applied on a set of 4000 sentences, and the results has been evaluated using the standard metrics, they are True positive, True negative, False positive, False negative, Precision, Recall, and F-score. We also provided a comparison of the presented work with previous work that has been presented in the same field. Index Terms—Opinion mining, opinion discovery, sentimental analysis, natural language processing

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Opinion Mining and Sentimental Analysis Approaches: A Survey - 01/0

Mahmoud Sami Abdelaziz Othman

Hesham Hassan, Ramadan Moawad, Abeer El-Korany

01/01/2014

The automatic extraction of information from unstructured sources has opened up new ways for querying, organizing, and analyzing data by building a clean semantics of structured databases from a huge number of unstructured data and the society became more data oriented with easy online access to both structured and unstructured data. New applications of structured extraction came around such as the paper topic opinion mining, which is a type of natural language processing for tracking the mood of the public about a particular topic. Opinion mining, which is also called sentiment analysis, involves building a system to collect and examine opinions about the product or topic made in blog posts, comments, reviews or tweets. Automated opinion mining often uses machine learning, which is a component of artificial intelligence (AI).

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Ontology-Based: Intelligent Document Management System - 01/0

Mahmoud Sami Abdelaziz Othman

Hesham Hassan

01/01/2011

As more and more knowledge and information becomes available through computers in the organizations, a critical capability of systems supporting knowledge management is the classification of documents into categories that are meaningful to the user. Today, text categorization is required due to the very large amount of text documents that we have to deal with daily. A text categorization system can be used in indexing documents to assist information retrieval tasks as well as in classifying documents of any specific Domain, This paper proposes an intelligent document management system that use the ontology of a specific domain to annotate the documents for classifying it automatically. The proposed system consists of seven main parts: Ontology Extractor, Ontology Parser, Document Parser, Annotator, Indexer, Data Repository, and Categorization Engine.

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Awards

Award Donor Date
Best Performance Future University 2018
Outstanding Service Future University 2016

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