| 000 | 00000nam u2200205 a 4500 | |
| 001 | 000045893843 | |
| 005 | 20170125160826 | |
| 008 | 170124s2017 maua b 001 0 eng d | |
| 020 | ▼a 9780128044124 | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 082 | 0 4 | ▼a 006.35 ▼2 23 |
| 084 | ▼a 006.35 ▼2 DDCK | |
| 090 | ▼a 006.35 ▼b S478 | |
| 245 | 0 0 | ▼a Sentiment analysis in social networks / ▼c edited by Federico Alberto Pozzi ... [et al.]. |
| 260 | ▼a Cambridge, MA : ▼b Elsevier : ▼b Morgan Kaufmann, ▼c c2017. | |
| 300 | ▼a xix, 263 p. ; : ▼b ill. ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Natural language processing (Computer science). |
| 650 | 0 | ▼a Computational linguistics. |
| 650 | 0 | ▼a Social networks. |
| 700 | 1 | ▼a Pozzi, Federico Alberto. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.35 S478 | 등록번호 111766586 (3회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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
- Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies
- Provides insights into opinion spamming, reasoning, and social network mining
- Shows how to apply opinion mining 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
정보제공 :
목차
Chapter 1: Challenges of Sentiment Analysis in Social Networks: An Overview
Chapter 2: Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis
Chapter 3: Semantic Aspects in Sentiment Analysis
Chapter 4: Linked Data Models for Sentiment and Emotion Analysis in Social Networks
Chapter 5: Sentic Computing for Social Network Analysis
Chapter 6: Sentiment Analysis in Social Networks: A Machine Learning Perspective
Chapter 7: Irony, Sarcasm, and Sentiment Analysis
Chapter 8: Suggestion Mining From Opinionated Text
Chapter 9: Opinion Spam Detection in Social Networks
Chapter 10: Opinion Leader Detection
Chapter 11: Opinion Summarization and Visualization
Chapter 12: Sentiment Analysis With SpagoBI
Chapter 13: SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies
Chapter 14: The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns
Chapter 15: Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements With Real-Time Multidimensional Opinion Streaming
Chapter 16: Conclusion and Future Directions
정보제공 :