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El. knyga: Prominent Feature Extraction for Sentiment Analysis

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  • Formatas: PDF+DRM
  • Serija: Socio-Affective Computing 2
  • Išleidimo metai: 14-Dec-2015
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319253435
Kitos knygos pagal šią temą:
Prominent Feature Extraction for Sentiment AnalysisDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Socio-Affective Computing 2
  • Išleidimo metai: 14-Dec-2015
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319253435
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The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model.Authors pay attention to the four main findings of the book :-Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection techniqu

e improves the performance of the sentiment analysis by eliminating the redundant features.- Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis.- The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis.- Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

1 Introduction2 Literature Survey3 Machine Learning Approach for Sentiment Analysis4 Semantic Parsing using Dependency Rules5 Sentiment Analysis using ConceptNet Ontology and ContextInformation6 Semantic Orientation based Approach for Sentiment Analysis7 Conclusions and FutureWorkReferencesGlossaryIndex
1 Introduction
1(4)
1.1 Motivation
3(2)
2 Literature Survey
5(16)
2.1 Machine Learning Approaches
5(8)
2.1.1 Text Preprocessing
6(2)
2.1.2 Feature Selection Methods
8(2)
2.1.3 Feature Weighting and Representation Schemes
10(2)
2.1.4 Machine Learning Algorithms
12(1)
2.2 Semantic Orientation-Based Approaches
13(6)
2.3 Conclusions
19(2)
3 Machine Learning Approach for Sentiment Analysis
21(26)
3.1 Feature Selection Techniques
22(3)
3.1.1 Minimum Redundancy Maximum Relevance
23(1)
3.1.2 Information Gain (IG)
24(1)
3.2 Machine Learning Methods
25(2)
3.2.1 Multinomial Naive Bayes
25(2)
3.2.2 Support Vector Machine
27(1)
3.3 Feature Extraction Methods
27(6)
3.3.1 Basic Features
27(2)
3.3.2 Prominent Features
29(1)
3.3.3 Composite Features
29(2)
3.3.4 Clustering Features
31(2)
3.4 Dataset, Experimental Setup, and Results
33(11)
3.4.1 Dataset Used
33(2)
3.4.2 Evaluation Metrics
35(1)
3.4.3 Results and Discussions
35(9)
3.5 Conclusions
44(3)
4 Semantic Parsing Using Dependency Rules
47(16)
4.1 ConceptNet
47(1)
4.2 Syntactic N-Grams (sn-Gram)
48(1)
4.3 Proposed Methodology
49(6)
4.3.1 Formation of Concepts Using Dependency Relations
51(3)
4.3.2 Obtaining Commonsense Knowledge from ConceptNet
54(1)
4.3.3 Optimal Feature Set Construction
54(1)
4.4 Example
55(1)
4.5 Results and Discussions
56(5)
4.5.1 Comparison with Existing Methods
58(3)
4.6 Conclusions
61(2)
5 Sentiment Analysis Using ConceptNet Ontology and Context Information
63(14)
5.1 WordNet
63(1)
5.2 Proposed Methodology
64(8)
5.2.1 Construction of Domain-Specific Ontology from ConceptNet
64(3)
5.2.2 Aspect Extraction
67(1)
5.2.3 Feature-Specific Opinion Extraction
67(1)
5.2.4 Construction of Contextual Polarity Lexicon
67(4)
5.2.5 Sentiment Aggregation
71(1)
5.3 Experiments and Results
72(3)
5.3.1 Dataset and Evaluation
72(1)
5.3.2 Experiments
72(2)
5.3.3 Results
74(1)
5.3.4 Comparison with Related Work
75(1)
5.4 Conclusions
75(2)
6 Semantic Orientation-Based Approach for Sentiment Analysis
77(12)
6.1 Proposed Approach
77(4)
6.1.1 Feature Extraction Methods
78(3)
6.2 Semantic Orientation
81(4)
6.2.1 Supervised Method
82(1)
6.2.2 Semisupervised Method
82(2)
6.2.3 Semantic Orientation Aggregation
84(1)
6.3 Experiment Result and Discussion
85(3)
6.3.1 Results
85(3)
6.4 Conclusions
88(1)
7 Conclusions and Future Work
89(4)
7.1 Conclusions
89(2)
7.1.1 Summary of Main Findings
90(1)
7.1.2 Contributions
90(1)
7.2 Future Works
91(2)
Glossary 93(2)
References 95(8)
Index 103
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