000			a
999			_c33008 _d33008
008			240318b xxu||||| |||| 00| 0 eng d
020			_a9783319553924
082			_a006.312 _bCAM
100			_aCambria, Erik _eed.
245			_aA practical guide to sentiment analysis
260			_bSpringer, _c2017 _aCham :
300			_avii, 196 p. ; _bill., (some col.), _c24 cm
365			_b149.99 _c€ _d93.50
490			_aSocio-affective computing ; _vv5
504			_aIncludes bibliographical references and index.
520			_aThis edited work presents studies and discussions that clarify the challenges and opportunities of sentiment analysis research. While sentiment analysis research has become very popular in the past ten years, most companies and researchers still approach it simply as a polarity detection problem. In reality, sentiment analysis is a 'suitcase problem' that requires tackling many natural language processing subtasks, including microtext analysis, sarcasm detection, anaphora resolution, subjectivity detection and aspect extraction. In this book, the authors propose an overview of the main issues and challenges associated with current sentiment analysis research and provide some insights on practical tools and techniques that can be exploited to both advance the state of the art in all sentiment analysis subtasks and explore new areas in the same context. Readers will discover sentiment mining techniques that can be exploited for the creation and automated upkeep of review and opinion aggregation websites, in which opinionated text and videos are continuously gathered from the Web and not restricted to just product reviews, but also to wider topics such as political issues and brand perception. The book also enables researchers to see how affective computing and sentiment analysis have a great potential as a sub-component technology for other systems. They can enhance the capabilities of customer relationship management and recommendation systems allowing, for example, to find out which features customers are particularly happy about or to exclude from the recommendations items that have received very negative feedbacks. Similarly, they can be exploited for affective tutoring and affective entertainment or for troll filtering and spam detection in online social communication.
650			_aComputational linguistics
650			_aInformation retrieval
650			_aMathematical and statistical software
650			_aMaths for engineers
650			_aMedical research
650			_aNatural Language Processing
650			_aUser interface design and usability
700			_aDas, Dipankar _eed.
700			_aBandyopadhyay, Sivaji _eed.
700			_aFeraco, Antonio _eed.
942			_2ddc _cBK