Nlp For Sentiment Analysis
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NLP FOR SENTIMENT ANALYSIS
Author: Prof. Dr. Dileep Kumar M.
language: en
Publisher: Xoffencer International Book Publication
Release Date: 2024-10-25
Natural Language Processing (NLP) has become a cornerstone in extracting and interpreting human emotions and opinions from text data, and one of its significant applications is sentiment analysis. Sentiment analysis aims to automatically identify subjective information within text, often categorizing sentiments as positive, negative, or neutral. This ability to quantify opinion and emotion has garnered interest from a broad range of industries—marketing, healthcare, finance, and customer service, to name a few—as organizations increasingly rely on insights derived from unstructured data like social media posts, reviews, and feedback forms. The rise in data-driven decision-making further underscores the importance of sentiment analysis, positioning it as a valuable tool in understanding public opinion, customer satisfaction, and user experience. With NLP, sentiment analysis transforms complex linguistic expressions into structured, analyzable data, enabling businesses and researchers to gauge public mood and predict behavior, thus facilitating more responsive and personalized services. Sentiment analysis is inherently challenging, however, as it requires deep comprehension of language structure, context, and the subtleties of human expression. Human language is diverse and laden with intricacies, including sarcasm, humor, regional dialects, and idiomatic expressions, which can complicate straightforward sentiment categorization. Modern sentiment analysis leverages a combination of machine learning, deep learning, and lexicon-based approaches to overcome these obstacles. Machine learning models like Support Vector Machines, Naive Bayes, and increasingly complex neural networks have been employed to classify sentiment, often with notable success. Deep learning, particularly through techniques such as Long Short-Term Memory (LSTM) networks and Transformer-based architectures like BERT and GPT, has further advanced sentiment analysis by enabling models to process long text sequences and capture contextual nuances. Lexicon-based approaches, on the other hand, involve predefined lists of words associated with sentiment, offering a more rule-based approach that can be useful in specific applications or as a complement to machine learning methods. In recent years, transfer learning has brought about substantial improvements in NLP for sentiment analysis, particularly through pretrained models that allow for fine-tuning on sentiment-specific tasks with minimal labeled data.
Sentiment Analysis and Opinion Mining
Author: Bing Liu
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2012-05-01
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography
Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications
Sentiment Analysis has become increasingly important in recent years for nearly all online applications. Sentiment Analysis depends heavily on Artificial Intelligence (AI) technology wherein computational intelligence approaches aid in deriving the opinions/emotions of human beings. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas. The applications of Sentiment Analysis are enormous, ranging from business to biomedical and clinical applications. However, the combination of AI methods and Sentiment Analysis is one of the rarest commodities in the literature. The literatures either gives more importance to the application alone or to the AI/CI methodology.Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The authors provide readers with an in-depth look at the challenges and solutions associated with the different types of Sentiment Analysis, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered, which will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. - Includes basic concepts, technical explanations, and case studies for in-depth explanation of the Sentiment Analysis - Aids computer scientists in developing practical/real-world AI-based Sentiment Analysis systems - Provides readers with real-world development applications of AI-based Sentiment Analysis, including transfer learning for opinion mining from pandemic medical data, sarcasm detection using neural networks in human-computer interaction, and emotion detection using the random-forest algorithm