Deep Learning-Based Approaches for Sentiment Analysis, Agarwal Basant, Nayak Richi, Mittal Namita
Автор: Dharmendra Singh Rajput, Ramjeevan Singh Thakur, S. Muzamil Basha Название: Sentiment Analysis and Knowledge Discovery in Contemporary Business ISBN: 1522549994 ISBN-13(EAN): 9781522549994 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31324.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals who are using them to collect vital data.Sentiment Analysis and Knowledge Discovery in Contemporary Business is an essential reference source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. Featuring research on topics such as knowledge retrieval and knowledge updating, this book is ideally designed for business managers, academicians, business professionals, researchers, graduate-level students, and technology developers seeking current research on data collection and management to drive profit.
Автор: Bo Pang Название: Opinion Mining and Sentiment Analysis ISBN: 1601981503 ISBN-13(EAN): 9781601981509 Издательство: Marston Book Services Рейтинг: Цена: 16335.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems.
Автор: Basant Agarwal; Namita Mittal Название: Prominent Feature Extraction for Sentiment Analysis ISBN: 3319253417 ISBN-13(EAN): 9783319253411 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
1 Introduction
2 Literature Survey
3 Machine Learning Approach for Sentiment Analysis
4 Semantic Parsing using Dependency Rules
5 Sentiment Analysis using ConceptNet Ontology and Context
Information
6 Semantic Orientation based Approach for Sentiment Analysis
7 Conclusions and FutureWork
References
Glossary Index
Автор: Basant Agarwal, Richi Nayak Название: Deep Learning-Based Approaches for Sentiment Analysis ISBN: 9811512159 ISBN-13(EAN): 9789811512155 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis.
Автор: Vadim Kagan; Edward Rossini; Demetrios Sapounas Название: Sentiment Analysis for PTSD Signals ISBN: 1461430968 ISBN-13(EAN): 9781461430964 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- Introduction to PTSD Signals.- Data Source.- Text Analytics.- Scoring Engine.- System Overview.- Conclusions.
Автор: Pozzi, Federico Название: Sentiment Analysis in Social Networks ISBN: 0128044128 ISBN-13(EAN): 9780128044124 Издательство: Elsevier Science Рейтинг: Цена: 7409.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking.
Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.
Further, this volume:
Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies
Provides insights into opinion spamming, reasoning, and social network analysis
Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences
Serves as a one-stop reference for the state-of-the-art in social media analytics
Автор: Ranjan Satapathy; Erik Cambria; Amir Hussain Название: Sentiment Analysis in the Bio-Medical Domain ISBN: 3319886096 ISBN-13(EAN): 9783319886091 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Поставка под заказ.
Автор: Liu, Bing (university Of Illinois, Chicago) Название: Sentiment analysis ISBN: 1108486371 ISBN-13(EAN): 9781108486378 Издательство: Cambridge Academ Рейтинг: Цена: 10611.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sentiment analysis is the computational study of people`s opinions, emotions, and attitudes. This comprehensive introduction covers all core areas useful for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. The second edition includes new deep learning analysis methods.
Автор: Rajput Dharmendra Singh, Thakur Ramjeevan Singh, Basha S. Muzamil Название: Sentiment Analysis and Knowledge Discovery in Contemporary Business ISBN: 1522587853 ISBN-13(EAN): 9781522587859 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 20691.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. The book features research on a wide range of topics, including knowledge retrieval and knowledge updating.
Описание: This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis.
Описание: This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
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