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Advances in social media analysis [electronic resource] / Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker, editors.

Gaber, Mohamed Medhat, editor.; Cocea, Mihaela, editor.; Wiratunga, Nirmalie, editor.; Goker, Ayse, editor.
2015
QA76.9.D343
Available Online
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Title
Advances in social media analysis [electronic resource] / Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker, editors.
ISBN
9783319184586 electronic book
331918458X electronic book
9783319184579
DOI
https://doi.org/10.1007/978-3-319-18458-6
Published
Cham : Springer, 2015.
Language
English
Description
1 online resource (vii, 151 pages) : illustrations.
Item Number
10.1007/978-3-319-18458-6 doi
Call Number
QA76.9.D343
Dewey Decimal Classification
006.3/12
Summary
This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis. .
Note
Includes author index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 12, 2015).
Added Author
Gaber, Mohamed Medhat, editor.
Cocea, Mihaela, editor.
Wiratunga, Nirmalie, editor.
Goker, Ayse, editor.
Series
Studies in computational intelligence ; 602.
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Case-Studies in Mining User-Generated Reviews for Recommendation
Mining Newsworthy Topics from Social Media
Sentiment Analysis Using Supervised Learning with Domain-Adaptation and Sentence-Based Analysis
Pattern-based Emotion Classification on Social Media
Entity-based Opinion Mining from Text and Multimedia
Predicting Emotion Labels for Chinese Microblog Texts.

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