| 000 | 03017nam a2200265 4500 | ||
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| 020 | _a 9780128044124 | ||
| 020 | _a9780128044384 | ||
| 040 | _aUTB | ||
| 100 | 1 |
_a Pozzi, Federico Alberto. _eauthor. |
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| 245 | 1 | 0 |
_aSentiment Analysis in Social Networks / _c Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu. |
| 260 |
_aCambridge, MA : _bMorgan Kaufmann, _c2017. |
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| 500 | _aThis book is automatically accessible within the University internet network. To learn how to access it outside the campus, visit: https://www.utb.edu.bn/media/cv5fcgwb/remote-access-for-ebscohost.pdf | ||
| 520 | _aThe 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 | ||
| 650 | 4 | _a Social networks | |
| 650 | 4 | _aNatural language processing (Computer science) | |
| 650 | 4 | _aComputational linguistics | |
| 700 | 1 |
_eauthor. _aFersini, Elisabetta. |
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| 700 | 1 |
_eauthor _aMessina, Enza. |
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| 700 | 1 |
_eauthor. _aBing Liu |
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| 856 | _uhttps://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1144691&site=ehost-live | ||
| 942 |
_2lc _cEBB |
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| 998 |
_eElectronic book _sReference No: 4736965 / 10.12.21 / ns277138 Date: 12 October 2021 -- (UTB) – EBSCO International, INC |
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| 999 |
_c21233 _d21233 |
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