# Introduction ## Semantic Web Vision / Motivation ## Purpose # Ontology # RDF (Resource Description Framework) ## Basics of RDF ## RDF model ## Resources Resources are objects, informally referred to as "things". Examples are authors, apartments, people, hotels and search queries. Every resource has a URI - a unique resource identifier. This could for example be an ISBN-number for a book, a URL for a web page or coordinates for a location. ## Properties The properties describe the relationships between the resources. For example Anna is a _friend of_ Bruce, Harry Potter and the Philosopher's Stone was _written by_ J.K. Rowling or Oslo is _located in_ Norway. The properties is marked with italic font. Properties are also described by a URI. ## Statements RDF statements assert the properties of resources and are in the form subject-predicate-object, a.k.a. a __triple__. The subject denotes the resource, the predicate expresses a relationship between the subject and the object. ## Serialization formats N-triples, Turle, RDF/XML, RDF/JSON.

# RDFS (Resource Description Framework Schema) ## Basics of RDFS ## Vocabulary ## Classes ## Properties ## Literals ## Problems with RDFS # SPARQL (SPARQL Protocol and RDF Query Language) ## Basics of SPARQL ## Graph traversal ## Queries # OWL (Web ontology language) ## Basics of OWL ## Language constructs ### Classes ### Properties ### Property characteristics ### Cardinality ### Individuals ### Others ## Semantics and Reasoning ### Description Logics # Ontology guidelines # Sentiment analysis ## Basics of Sentiment analysis ## Document level sentiment classification ## Sentence level sentiment classification ## Opinion lexicon generation ## Aspect-based opinion mining ## Opinion mining of comparative sentences ## Opinion spam detection ## Unsupervised search-based approach ## Unsupervised lexicon-based approach # Recommender systems ## Problem domain ## Purpose and success criteria ## Paradigms of recommender systems ## Collaborative filtering ## Content-based flitering ## Semantic Vector space model # Articles / Books ## Sentiment Analysis and Opinion Mining Retrieved from the book _Sentiment Analysis and Opinion Mining_ by Bing Liu, published by Morgan & Claypool Publishers, May 2012. ### Preface There has been an increase in opinion based data on net, in the form of blog posts, Twitter, Facebook and so on. Therefore there has been more focus on sentiment analysis of data, as this can significantly improve these products. ### Sentiment Analysis: A Fascinating problem Sentiment analysis, or opinion mining, is the study of peoples opinions and attitudes in relation to products, organizations, people and so on. Sentiment analysis has a lot of names: opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysys, emotion analysis, review mining. ### Sentiment Analysis Applications Our opinions affect which decisions we make. This is why opinion mining is popular, since corporations are interested in getting their customers opinions on their products and services. If a company is able to map this they will have a competitive advantage. With "explosions" of opinion data in social media and similar applications it is easy to gather data for use in decisionmaking. It is not always necessary to create questionnaires to get peoples opinions. It can, however, be difficult to extract meaning from long blogposts and arcticles, and make a summary. This is why we need _Automated Sentiment analysis systems_. Sentiment analysis has spread to many different areas. ### Sentiment Analysis Research #### Different Levels of Analysis Research has mainly been done on three different levels. __Document-level sentiment classification__ works with entire documents and tries to figure out of the document has a positive or negative view of the subject in question. __Sentence-level sentiment classification__ works with opinion on a sentence level, and is about figuring out if a sentence is positive, negative or neutral to the relevant subject. This is closely related to _subjectivity classification_, which distinguishes between objective and subjective sentences. __Entity and aspect-level sentiment classification__ wants to discover opinions about an entity or it's aspects. For instance we have the followingsentence, "The iPhone’s call quality is good, but its battery life is short", to aspects are evaluated: call quality and battery lifetime. The entity is the iPhone. The iPhone's call quality is positive, but the battery duration is negative. It's hard to find and classify these aspects, as there are many ways to express positive and negative opinions, metaphors, comparisons and so on. #### Sentiment Lexicon and Its Issues Some words can be identified as either positive or negative immediately, like good, bad, excellent, terrible and so on. There are also subsentences/phrases that can be identified as positive or negative. A list of such words or sentences is called a sentiment lexicon. Use of just this is not enough. Below are several known issues: 1. Words and phrases can have different meaning in different contexts. For example, "suck" usually has a negative meaning, but can be positive if put in the right context: "This vacuum cleaner really sucks". 2. Sentences containing sentiment words sometimes do not reflect a sentiment. For example, "If this product X contain a _great_ feature Y, i'll buy it.". _great_ do not express a positive or negative opinion on the product X. 3. Sarcastic sentences are hard to deal with. They are mostly found in political discussions, e.g. "What a awesome product! It stopped working in two days". 4. Sentences without sentiment words can also express opinions. "This laptop consumes a lot of power.", reflects a partially negative opinion about the laptop, but the sentence is also objective as it states a fact.