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EXPLORING ASPECT-BASED SENTIMENT ANALYSIS IN THE ERA OF BIG DATA
Using sentiment analysis for purposes such as marketing, analysing consumer feedback, monitoring social media, and mining public opinion has garnered a lot of interest in recent years. One kind of sentiment analysis, known as aspect-based sentiment analysis (ABSA), zeroes attention on the emotions associated with certain elements of a dataset (such as a person, place, thing, etc.) rather than the whole. With the increasing availability of big data, ABSA has become even more relevant and challenging.
The authors of "Exploring Aspect-Based Sentiment Analysis in the Era of Big Data" set out to write a book that would serve as an accessible introduction to the developments and difficulties of ABSA in the era of big data. The book is organized into ten chapters, each addressing a specific aspect of ABSA.
Chapter 1 discusses the impact of domain adaptation on the ABSA of big data. Domain adaptation is an important technique to adapt ABSA models to new domains or languages, This is essential when applying it in the actual world. Methods for gauging the efficacy of ABSA models on massive datasets are presented in Chapter 2, which is crucial for ensuring the accuracy and reliability of ABSA systems.
Chapter 3 highlights the role of ABSA in social media monitoring and analysis, which is becoming increasingly important for understanding public opinion and sentiment towards different topics. Chapter 4 focuses on best practices for building a labelled dataset for ABSA on big data, which is critical in developing accurate and robust ABSA models.
Chapter 5 presents approaches for detecting sarcasm and irony in the ABSA of big data, which is challenging due to the complexity and ambiguity of such expressions. Chapter 6 discusses the importance of context in the ABSA of big data, which is critical for accurately identifying the sentiment towards specific aspects or entities in different contexts.
Chapter 7 presents techniques for ABSA of short texts on big data, which is challenging due to the limited context and information available in short texts, such as tweets and comments. Chapter 8 highlights the role of sentiment analysis in aspect-based entity recognition on big data, which is an important task for identifying and extracting entities and their associated sentiments in different domains.
A growing concern in the realm of data privacy and protection is how such regulations would affect the ABSA of big data, which happens to be the topic of Chapter 9. Chapter 10 presents approaches for ABSA of audio and video data on big data, an emerging research area with many applications in different domains.
Overall, this book provides a comprehensive overview of the recent advances and challenges in ABSA in the age of big data. It is intended for researchers, students, researchers, developers, and big data analytics, NLP, and sentiment analysis. We hope this book will stimulate further research and development in ABSA and inspire new ideas and applications in this exciting field.
The authors of "Exploring Aspect-Based Sentiment Analysis in the Era of Big Data" set out to write a book that would serve as an accessible introduction to the developments and difficulties of ABSA in the era of big data. The book is organized into ten chapters, each addressing a specific aspect of ABSA.
Chapter 1 discusses the impact of domain adaptation on the ABSA of big data. Domain adaptation is an important technique to adapt ABSA models to new domains or languages, This is essential when applying it in the actual world. Methods for gauging the efficacy of ABSA models on massive datasets are presented in Chapter 2, which is crucial for ensuring the accuracy and reliability of ABSA systems.
Chapter 3 highlights the role of ABSA in social media monitoring and analysis, which is becoming increasingly important for understanding public opinion and sentiment towards different topics. Chapter 4 focuses on best practices for building a labelled dataset for ABSA on big data, which is critical in developing accurate and robust ABSA models.
Chapter 5 presents approaches for detecting sarcasm and irony in the ABSA of big data, which is challenging due to the complexity and ambiguity of such expressions. Chapter 6 discusses the importance of context in the ABSA of big data, which is critical for accurately identifying the sentiment towards specific aspects or entities in different contexts.
Chapter 7 presents techniques for ABSA of short texts on big data, which is challenging due to the limited context and information available in short texts, such as tweets and comments. Chapter 8 highlights the role of sentiment analysis in aspect-based entity recognition on big data, which is an important task for identifying and extracting entities and their associated sentiments in different domains.
A growing concern in the realm of data privacy and protection is how such regulations would affect the ABSA of big data, which happens to be the topic of Chapter 9. Chapter 10 presents approaches for ABSA of audio and video data on big data, an emerging research area with many applications in different domains.
Overall, this book provides a comprehensive overview of the recent advances and challenges in ABSA in the age of big data. It is intended for researchers, students, researchers, developers, and big data analytics, NLP, and sentiment analysis. We hope this book will stimulate further research and development in ABSA and inspire new ideas and applications in this exciting field.
105 pages, Paperback
Published June 19, 2023
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