MoArLex: (Record no. 8818)

MARC details
000 -LEADER
fixed length control field 07865nam a22002537a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210112b2019 a|||f mb|| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mohab Youssef Shawky
245 1# - TITLE STATEMENT
Title MoArLex:
Remainder of title An Arabic Sentiment Lexicon Built Through Automatic Lexicon Expansion /
Statement of responsibility, etc. Mohab Youssef Shawky
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent 88 p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: Mohamed A. El-Helw
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2019 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/>CHAPTER I Introduction ................................................................................................ 14<br/>1.1 Problem Definition .................................................................................................. 14<br/>1.2 Objectives ................................................................................................................ 15<br/>1.3 Motivation ............................................................................................................... 15<br/>1.4 Thesis organization ................................................................................................. 17<br/>1.5 Publication related to this work .............................................................................. 17<br/>CHAPTER II Literature Review ...................................................................................... 18<br/>2.1 The beginning of Arabic Sentiment Lexicons ........................................................ 18<br/>2.2 Current alternative Arabic lexicons ........................................................................ 19<br/>2.2.1 Arabic sentiment analysis: Lexicon-based and Corpus-based [27] ................. 19<br/>2.2.2 Automatic expandable large-scale sentiment lexicon of Modern Standard Arabic and Colloquial [36] ....................................................................................... 23<br/>2.2.3 SANA: A Large-Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis [18] ................................................................. 26<br/>2.2.4 A Proposed Sentiment Analysis Tool for Modern Arabic ............................... 28<br/>vi<br/>Using Human-Based Computing [40] ....................................................................... 28<br/>2.2.5 Mining Arabic Business Reviews [41] ............................................................ 30<br/>2.2.6 Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis [44] ............................................................................................ 32<br/>2.2.7 BiSAL - A bilingual sentiment analysis lexicon to analyse Dark Web forums for cybersecurity [46] ................................................................................................ 35<br/>2.2.8 NileULex: A Phrase and Word Level Sentiment Lexicon for Egyptian and Modern Standard Arabic [10] ................................................................................... 39<br/>2.2.9 Sentiment Lexicons for Arabic Social Media [16] .......................................... 44<br/>2.2.9 A web-based tool for Arabic sentiment analysis [58] ...................................... 48<br/>2.2.10 Lexicon-Based Sentiment Analysis of Arabic Tweets [65] ........................... 51<br/>2.2.11 Improving Sentiment Analysis in Arabic Using Word Representation [67] 54<br/>2.2 Summary of Lexicon Expansion Works ........................................................... 57<br/>2.3 Conclusion .............................................................................................................. 59<br/>CHAPTER III The Proposed System: Arabic Sentiment Lexicon Construction and Sentiment Analysis Tool ................................................................................................... 60<br/>3.1 Methodology ........................................................................................................... 60<br/>3.1.1 Lexicon Expansion ........................................................................................... 60<br/>3.1.1.1 Generation of candidate terms .................................................................. 62<br/>3.1.1.2 Filtering of candidate terms ...................................................................... 64<br/>3.1.1.3 Polarity determination ............................................................................... 65<br/>3.1.1.4 Time taken for building the lexicon .......................................................... 69<br/>3.1.1.5 Hardware & software used for building lexicon ....................................... 69<br/>3.2 Main features of the presented lexicon ................................................................... 69<br/>vii<br/>3.3 A Simple Sentiment Analysis Tool for Lexicon Evaluation ................................... 70<br/>CHAPTER IV Evaluation ................................................................................................. 73<br/>Introduction ................................................................................................................... 73<br/>4.1 Manual Evaluation of the Lexicon .......................................................................... 73<br/>4.2 Comparison between MoArLex and other lexicons ............................................... 76<br/>4.3 Supervised Learning Experiment ............................................................................ 79<br/>4.4 Evaluating the Lexicon through Sentiment Analysis .............................................. 80<br/>4.5 Conclusion .............................................................................................................. 80<br/>CHAPTER V Conclusion and Future Work ..................................................................... 82<br/>5.1 Conclusion .............................................................................................................. 82<br/>5.2 Future Work ............................................................................................................ 82<br/>References .........................................................................................................................
520 3# - Abstract
Abstract Abstract:<br/>Research addressing Sentiment Analysis has witnessed great attention over the last decade especially after the huge increase in social media network usage. Social networks like Facebook and Twitter generate an incredible amount of data on a daily basis, containing posts that discuss all kinds of different topics ranging from sports and products to politics and current events. Since data generated within these mediums are created by users from all over the world, it is multilingual in nature. Arabic is one of the important languages recently targeted by many sentiment analysis efforts. However, Arabic is considered to be under-resourced in terms of lexicons and datasets when compared to English.<br/>This work presents a novel technique for automatically expanding an Arabic sentiment lexicon using word embeddings. The main aim of this work is to build high coverage Arabic lexicon automatically. The main objective of this work is to use the built Arabic lexicon in a sentiment analysis task. Moreover, the proposed system is designed to overcome the low accuracy problem of the other automatically expanded Arabic lexicons.<br/>Evaluation of the quality of the automatically added terms was done in multiple ways, all of which have shown that lexicon entries added using the presented way are more accurate than sentiment lexicon entries obtained using machine learning or distant supervision methods.
546 ## - Language Note
Language Note Text in English, abstracts in English.
650 #4 - Subject
Subject Informatics-IFM
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School Informatics-IFM
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
650 #4 - Subject
-- 266
655 #7 - Index Term-Genre/Form
-- 187
690 ## - Subject
-- 266
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification   Not For Loan Main library Main library 01/12/2021   610 / M.Y.M / 2019 01/12/2021 01/12/2021 Thesis