adsm كلية أبوظبي للإدارة ABU DHABI SCHOOL OF MANAGEMENT Sentiment Analysis using Artificial Intelligence for governmental organization e-services AM Supervisor Dr. Neda Abdelhamid Master of Science in Business Analytics (2022-2023) The candidate confirms that the work submitted is their own and appropriate credit has been given where reference has been made to the work of others. 1 adsm كلية أبــوظــبــي للإدارة ABU DHABI SCHOOL OF MANAGEMENT Sentiment Analysis using Artificial Intelligence for governmental organization e-services Supervisor Dr. Neda Abdelhamid A project presented to ABU DHABI SCHOOL OF MANAGEMENT In partial fulfilment of the requirement for the degree of Master of Science in Business Analytics (2022-2023) 2 AI ANN API CRISP-DM ᎠᏴ HTTP Abbreviations A sample of abbreviations is given below. The student should important abbreviations according to their project Artificial Intelligence Artificial Neural Network Application Programming Interface Cross-Industry Standard Process for Data Mining Database Hypertext Transfer Protocol IoT Internet of Things JSON JavaScript Object Notation LSTM Long Short-Term Memory ML NLP NoSQL PCA REST RNN SaaS SQL SVM TF-IDF UAE TAMM ADDA NLP CRISP-DM NLTK SVM Tweepy Machine Learning Natural Language Processing Not Only SQL Principal Component Analysis Representational State Transfer Recurrent Neural Network Software as a Service Structured Query Language Support Vector Machine Term Frequency-Inverse Document Frequency United Arab Emirates AbuDhabi E-Services Government Platform Abu Dhabi Digital Authority Natural Language Processing Cross-Industry Standard Process for Data Mining Natural Language Toolkit Support Vector Machines an open source Python package that gives you a very convenient way to access the Twitter API with Python 3 699 889aa0 .7 .8 .8 .10 .10 .10 .11 .11 .12 2 .12 .12 22 .12 .13 Table of Contents Abbreviations Chapter 1 Motivation Background Related work... Problem Statement.. Approach and methodology Table of Contents 1. Business Understanding (CRISP-DM Phase 1): 2. Data Understanding (CRISP-DM Phase 2): 3. Data Preparation (CRISP-DM Phase 3):. 4. Modeling (CRISP-DM Phase 4):. 5. Evaluation (CRISP-DM Phase 5):.. WIPLE 6. Deployment (CRISP-DM Phase 6): Scope and limitations Target group Literature Review Chapter 2 (Sentiment Analysis using Artificial Intelligence for governmental organization e-services). Introduction. Introduction to Abu Dhabi E-Services Platform (TAMM) Sentiment Analysis in E-services:. Crisp-DM for sentiment analysis.... Tweepy. Machine learning for sentiment analysis. Sentiment Analysis using Arabic Language.. Literature Review Table. Chapter 3 (Methodology). Methodology 1. Business Understanding: . 2. Data Understanding:. Tweet Flash Exploratory Data Analysis (EDA). 3. Data Preparation: 4. Modeling: Term Frequency inverse document frequency TF-IDF . 4 .14 1567222222222 .19 .20 .20 .23 .23 Overfitting in machine learning. Sentiment Analysis of Twitter TAMM Tweets: An Approach Data Acquisition and Normalization .24 .25 .25 Data Preparation. Model Training and Validation. Machine Learning Model Selection .25 .25 .26 Outcome Performance Evaluation and Model Optimization. Chapter 4 (Data Analysis) Evaluation..... Apply the trained ML algorithms to our Tweets. Analyzing the output of our results. Overall general comparison. Pairwise Classifier Agreement Heatmap. Chapter 5 (Results and Findings). MLP Classifier Word Cloud Analysis Findings. Analysis in depth using the context of frequent words.. Chapter 6 (Discussion). Sentiment analysis for decision making process.. Hypothesis Validation.. H1: Sentiment H2: CRISP-DM as a Structured Methodology for Sentiment Analysis. H3: Precision of Sentiment Chapter 7 (Conclusion)... Future work References S AMPLE .26 .26 .26 .29 .29 .29 .31 .35 .35 .36 .38 .39 .40 .41 Analysis as a Tool for Enhancing E-Service User Experience .41 .42 Classifications Through Machine Learning Techniques.. .42 .43 44 .45 5/n Student Note: This is chapter 7. To be done in 1000 words. You already did chapter 1-6.