Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall tonality of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). This book is your ultimate resource for Sentiment Analysis. Here you will find the most up-to-date information, analysis, background and everything you need to know. In easy to read chapters, with extensive references and links to get you to know all there is to know about Sentiment Analysis right away, covering: Sentiment analysis, Natural language processing, Affix grammar over a finite lattice, AFNLP, Aggregation (linguistics), Attensity, Automatic Acquisition of Sense-Tagged Corpora, Automatic summarization, Bag of words model, Bigram, Brill tagger, Cache language model, Calais (Reuters Product), ChaSen, ClearForest, CMU Pronouncing Dictionary, Computational semantics, Computerized Speech Lab, Concept mining, Content determination, Controlled natural language, Cross-language information retrieval, DATR, Discourse relation, Document classification, Document-term matrix, ETBLAST, Filtered-popping recursive transition network, Robby Garner, GeneRIF, Gorn address, Grammar checker, Grammar induction, Grammatik, History of machine translation, History of natural language processing, IGlue, Information extraction, Information retrieval, International Conference on Language Resources and Evaluation, Kleene star, Language Computer Corporation, Language guessing, Language identification, Languageware, Latent semantic analysis, Latent semantic mapping, Legal information retrieval, Lesk algorithm, Lexalytics, Document structuring, Lexical choice, Lexical Markup Framework, Lexical substitution, Linguistic Issues in Language Technology, LKB, Logic form, Machine translation software usability, Example-based machine translation, MAREC, METEOR, Microsoft text-to-speech voices, Modular Audio Recognition Framework, Morphological pattern, Multi-document summarization, Multilingual notation, N-gram, Paco Nathan, Natural language, Natural language user interface, News analytics, Noisy text analytics, NooJ, Ontology learning, Open domain question answering, Powerset (company), Production (computer science), PropBank, Question answering, Realization (linguistics), Recursive transition network, Referring expression generation, Rewrite rule, Semantic analysis (computational), Semantic analytics, Semantic neural network, SemEval, Sentence extraction, Sentic computing, Shapado, SHRDLU, Speech segmentation, SPL notation, Stemming, String kernel, Studies in NLP, Sukhotins Algorithm, Synthetix, T9 (predictive text), Tatoeba, Teragram Corporation, Terminology extraction, Natural Language Toolkit, Text analytics, Text mining, Text Retrieval Conference, Text simplification, Text-to-voice, TipTop Technologies, Transderivational search, Trigram, Triphone, W-shingling, William Aaron Woods, Word-sense disambiguation, Word sense induction This book explains in-depth the real drivers and workings of Sentiment Analysis. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Sentiment Analysis with the objectivity of experienced professionals.