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 technique 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.
