Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

https://www.foo.be/cours/dess-20122013/b/Natural%20Language%20Processing%20with%20Python%20-%20O'Reilly2009.pdf

Natural Language Processing with Python

As a novel attack on the perennially vexing questions of the theoretical status of thematic roles and the inventory of possible roles, this paper defends a strategy of basing accounts of roles on more unified domains of linguistic data than have been used in the past to motivate roles, addressing in particular the problem of ARGUMENT SELECTION (principles determining which roles are associated with which grammatical relations). It is concluded that the best theory for describing this domain is not a traditional system of discrete roles (Agent, Patient, Source, etc.) but a theory in which the only roles are two cluster-concepts called PROTO-AGENT and PROTO-PATIENT, each characterized by a set of verbal entailments: an argument of a verb may bear either of the two proto-roles (or both) to varying degrees, according to the number of entailments of each kind the verb gives it. Both fine-grained and coarse-grained classes of verbal arguments (corresponding to traditional thematic roles and other classes as well) follow automatically, as do desired 'role hierarchies'. By examining occurrences of the 'same' verb with different argument configurations—e.g. two forms of psych predicates and object-oblique alternations as in the familiar spray/load class—it can also be argued that proto-roles act as defaults in the learning of lexical meanings. Are proto-role categories manifested elsewhere in language or as cognitive categories? If so, they might be a means of making grammar acquisition easier for the child, they might explain certain other typological and acquisitional observations, and they may lead to an account of contrasts between unaccusative and unergative intransitive verbs that does not rely on deriving unaccusatives from underlying direct objects.

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Thematic proto-roles and argument selection

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.

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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

The generic term fuzzy quantifier is employed in this paper to denote the collection of quantifiers in natural languages whose representative elements are: several, most, much, not many, very many, not very many, few, quite a few, large number, small number, close to five, approximately ten, frequently, etc. In our approach, such quantifiers are treated as fuzzy numbers which may be manipulated through the use of fuzzy arithmetic and, more generally, fuzzy logic.

A concept which plays an essential role in the treatment of fuzzy quantifiers is that of the cardinality of a fuzzy set. Through the use of this concept, the meaning of a proposition containing one or more fuzzy quantifiers may be represented as a system of elastic constraints whose domain is a collection of fuzzy relations in a relational database. This representation, then, provides a basis for inference from premises which contain fuzzy quantifiers. For example, from the propositions “Most U's are A's” and “Most A's are B's,” it follows that “Most2 U's are B's,” where most2 is the fuzzy product of the fuzzy proportion most with itself.

The computational approach to fuzzy quantifiers which is described in this paper may be viewed as a derivative of fuzzy logic and test-score semantics. In this semantics, the meaning of a semantic entity is represented as a procedure which tests, scores and aggregates the elastic constraints which are induced by the entity in question.

A computational approach to fuzzy quantifiers in natural languages

1. Introduction.- 2. The Syntax and Semantics of Two Simple Languages.- I. The Language L0.- 1. Syntax of L0.- 2. Semantics of L0.- II. The Language L0E.- 1. Syntax of L0E.- 2. Semantics of L0E.- 3. Alternative Formulations of L0E and L0.- III. A Synopsis of Truth-Conditional Semantics.- IV. The Notion of Truth Relative to a Model.- V. Validity and Entailment Defined in Terms of Possible Models.- VI. Model Theory and Deductive Systems.- Exercises.- Note.- 3. First-Order Predicate Logic.- I. The Language L1.- 1. Syntax of L1.- 2. Semantics of L1.- II. The Language L1E.- 1. Syntax of L1E.- 2. Semantics of L1E.- Exercises.- Notes.- 4. A Higher-Order Type-Theoretic Language.- I. A Notational Variant of L1.- II. The Language Ltype.- 1. Syntax of Ltype.- 2. Semantics of Ltype.- III. Lambda Abstraction and the Language L?.- Exercises.- Notes.- 5. Tense and Modal Operators.- I. Tense Operators and Their Interpretation.- II. The Other Varieties of Modal Logic the Operators ? and ?.- III. Languages Containing Both Tense and Modal Operators: Coordinate Semantics.- Exercises.- Notes.- 6. Montague's Intensional Logic.- I. Compositionality and the Intension-Extension Distinction.- II. The Intensional Logic of PTQ.- 1. Syntax of IL.- 2. Semantics of IL.- III. Examples of 'Oblique Contexts' as Represented in IL.- IV. Some Unresolved Issues with Possible Worlds Semantics and Propositional Attitudes.- Notes.- 7. The Grammar of PTQ.- I. The Overall Organization of the PTQ Grammar.- 1. The Syntactic Categories of English in the PTQ Grammar.- 2. The Correspondence Between Categories of English and Types of IL.- II. Subject-Predicate and Determiner-Noun Rules.- III. Conjoined Sentences, Verb Phrases, and Term Phrases.- IV. Anaphoric Pronouns as Bound Variables Scope Ambiguities and Relative Clauses.- V. Be, Transitive Verbs, Meaning Postulates, and Non-Specific Readings.- VI. Adverbs and Infinitive Complement Verbs.- VII. De dicto Pronouns and Some Pronoun Problems.- VIII. Prepositions, Tenses, and Negation.- Exercises.- Notes.- 8. Montague's General Semiotic Program.- 9. An Annotated Bibliography of Further Work in Montague Semantics.- Appendix I: Index of Symbols.- Appendix II: Variable Type Conventions for Chapter 7.- Notes.- References.- Answers to Selected Problems and Exercises.

Introduction to Montague semantics

Preface. Part A. Set Theory. 1. Basic Concepts of Set Theory. 2. Relations and Functions. 3. Properties of Relations. 4. Infinities. Appendix A1. Part B. Logic and Formal Systems. 5. Basic Concepts of Logic. 6.Statement Logic. 7. Predicate Logic. 8. Formal Systems, Axiomatization, and Model Theory. Appendix B1. Appendix BII. Part C. Algebra. 9. Basic Concepts of Algebra. 10. Operational Structures. 11. Lattices. 12. Boolean and Heyting Algebras. Part D. English as a Formal Language. 13. Basic Concepts of Formal Languages. 14. Generalized Quantifiers. 15. Intensionality. Part E. Languages, Grammars, and Automata. 16. Basic Concepts of Languages, Grammars, and Automata. 17. Finite Automata, Regular Languages and Type 3 Grammars. 18. Pushdown Automata, Context-Free Grammars and Languages. 19. Turing Machines, Recursively Enumberable Languages, and Type 0 Grammars. 20. Linear Bounded Automata, Context-Sensitive Languages and Type 1 Grammars. 21. Languages Between Context-Free and Context-Sensitive. 22. Transformational Grammars. Appendix EI. Appendix EII. Review Problems. Index.

Mathematical Methods in Linguistics

Steedman (1985, 1987), Dowty (1987), Moortgat (1988), Morrill (1988), and others have proposed that Categorial Grammar, a theory of syntax in which grammatical categories are viewed as functional types, be generalized in order to analyze “noncanonical” natural language constructions such as whextraction and nonconstituent conjunction. A consequence of these augmentations is an explosion of semantically equivalent derivations admitted by the grammar, a problem we have characterized as spurious ambiguity from the parsing perspective (Wittenburg, 1986). In Wittenburg (1987), it was suggested that the offending rules of these grammars could take an alternate predictive form that would eliminate the problem of spurious ambiguity. This approach, consisting of compiling grammars into forms more suitable for parsing, is within the tradition of discovering normal forms for phrase structure grammars, and thus our title. Our approach stands in contrast to those which are attempting to address the spurious ambiguity problem in Categorial Grammars through the parsing algorithm itself rather than through the grammar (see Gardent & Bes, 1989; Pareschi & Steedman, 1987) and also to those addressing the problem by proof-theoretic means in the Lambek calculus tradition (Bouma, 1989; Hepple & Morrill, 1989; Koenig, 1989; Lambek, 1958; Moortgat, 1986, 1988). We follow the line of Steedman (1985, 1987), Dowty (1987), and various strains of Categorial Unification Grammar (Karttunen, 1986; Uszkoreit, 1986; Wittenburg, 1986; Zeevat, Klein & Calder, 1987) in that we assume a finite number of combinatory rules and study the behavior of parsers that apply these rewrite rules in roughly the phrase-structure parsing tradition.

Parsing with Categorial Grammar in Predictive Normal Form

An algebra A is a set A together with one or more operations f i . We may represent an algebra by writing 

$${\rm{ }}A = \left\langle {A,{\rm{ }}{f_1},{\rm{ }}{f_2}{\rm{ }}...{\rm{, }}{f_n}} \right\rangle$$

(9-1)

Basic Concepts of Algebra

Extensions to Categorial Grammars proposed to account for nonconstitutent conjunction and long-distance dependencies introduce the problem of equivalent derivations, an issue we have characterized as spurious ambiguity from the parsing perspective. In Wittenburg (1987) a proposal was made for compiling Categorial Grammars into predictive forms in order to solve the spurious ambiguity problem. This paper investigates formal properties o f grammars that use predictive versions of function composition. Among our results are (1) that grammars with predictive composition are in general equivalent to the originals if and only if a restriction on predictive rules is applied, (2) that modulo this restriction, the predictive grammars have indeed eliminated the problem of spurious ambiguity, and (3) that the issue o f equivalence is decidable, i.e., for any particular grammar, whether one needs to apply the restriction or not to ensure equivalence is a decidable question.

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Robert E. Wall

Papers

Introduction to Montague semantics

Mathematical Methods in Linguistics

Parsing with Categorial Grammar in Predictive Normal Form

Basic Concepts of Algebra

Predictive Normal Forms for Composition in Categorical Grammars.