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This book covers deep-learning-based approaches for sentiment
analysis, a relatively new, but fast-growing research area, which
has significantly changed in the past few years. The book presents
a collection of state-of-the-art approaches, focusing on the
best-performing, cutting-edge solutions for the most common and
difficult challenges faced in sentiment analysis research.
Providing detailed explanations of the methodologies, the book is a
valuable resource for researchers as well as newcomers to the
field.
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.
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.
This book covers deep-learning-based approaches for sentiment
analysis, a relatively new, but fast-growing research area, which
has significantly changed in the past few years. The book presents
a collection of state-of-the-art approaches, focusing on the
best-performing, cutting-edge solutions for the most common and
difficult challenges faced in sentiment analysis research.
Providing detailed explanations of the methodologies, the book is a
valuable resource for researchers as well as newcomers to the
field.
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