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Bias Detection Based on Text Mining Technique : A case Study of Western Media Coverage for Palestinian-Israeli Conflict
Chapter 2 Literature Review8
2.1 Background:9
2.1.1 Palestine and Israel Conflict:9
2.1.1.1 History of Palestine-Israel Conflict:9
2.1.1.2 2014 Palestine-Israel Conflict9
2.1.1.3 Media coverage of 2014 Palestine-Israel conflict:10
2.1.2 News Domain:10
2.1.2.1 News Articles:10
2.1.2.2 News Bias:11
2.1.3 Text Data Mining:11
2.1.3.1 Text Mining:11
2.1.3.2 Sentiment Analysis:12
2.1.3.2.1 Ddifferent levels of Sentiment Analysis:12
2.1.3.2.2 Sentiment analysis Approaches13
2.1.4 Text Mining for New Bias Detection13
2.2 Related Works:14
2.2.1 Media Analytic:14
2.2.2 Text Analytic:15
2.2.3 Text Mining Bias Detection:16
Chapter 3 Research Methodology20
3.1 Methodology Overview:20
3.2 News Articles:21
3.3 Pre-processing:22
3.3.1 Tokenization:22
3.3.2 Normalization:22
3.3.3 Stop word removal:22
3.3.4 Stemming:22
3.3.5 Grams Generation:22
3.3.6 Weighting:22
3.4 Machine Learning Algorithms:23
3.4.1 Naïve Bias Classifier (NB):23
3.4.2 Support Vector Machine (SVM):23
3.4.3 Logisstic Regression (LR):24
3.5 Text Classification (TC):24
3.6 Evaluation:24
3.6.1 Subjective Evaluation:24
3.6.2 Objective Evaluation:25
3.6.2.1 Accuracy:25
3.6.2.2 Precision:25
3.6.2.3 Recall:25
3.6.2.4 F-measure:26
Chapter 4 Experiments28
4.1 Dataset:28
4.2 Training Data:29
4.2.1 Keywords:29
4.2.2 Quotes:30
4.2.3 Articles:31
4.3 Experiments Setup:32
4.3.1 Experimental Environment and Tools32
4.4 Experiments:33
4.4.1 Preprocessing:33
4.4.2 Supervised Machine Learning Algorithms:34
4.4.2.1 Keywords Classifier:34
4.4.2.2 Quotes:35
4.4.2.3 Articles:36
4.5 Bias Detection of News:37
4.5.1 Pro-Israel Bias:37
4.5.2 Pro-Palestinian38
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