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Online Content Analysis
Online content analysis or online textual analysis refers to a collection of research techniques used to describe and make inferences about online material through systematic coding and interpretation. Online content analysis is a form of content analysis for analysis of Internet-based communication. History and definition Content analysis as a systematic examination and interpretation of communication dates back to at least the 17th century. However, it was not until the rise of the newspaper in the early 20th century that the mass production of printed material created a demand for quantitative analysis of printed words. Berelson’s (1952) definition provides an underlying basis for textual analysis as a "research technique for the objective, systematic and quantitative description of the manifest content of communication." Content analysis consists of categorizing units of texts (i.e. sentences, quasi-sentences, paragraphs, documents, web pages, etc.) according to their subst ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Content Analysis
Content analysis is the study of documents and communication artifacts, which might be texts of various formats, pictures, audio or video. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is its non-invasive nature, in contrast to simulating social experiences or collecting survey answers. Practices and philosophies of content analysis vary between academic disciplines. They all involve systematic reading or observation of texts or artifacts which are assigned labels (sometimes called codes) to indicate the presence of interesting, meaningful pieces of content. By systematically labeling the content of a set of texts, researchers can analyse patterns of content quantitatively using statistical methods, or use qualitative methods to analyse meanings of content within texts. Computers are increasingly used in content analysis to aut ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from ' consisting of a set of ''training examples''. In supervised learning, each example is a ''pair'' consisting of an input object (typically a vector) and a desired output value (also called the ''supervisory signal''). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Leah Findlater
Leah K. Findlater is a Canadian-American computer scientist specializing in human-computer interaction, mobile computing, and computer accessibility. She is an associate professor of computer science at the University of Washington. Education Findlater studied computer science at the University of Regina, graduating with high honors in 2001. She went to the University of British Columbia (UBC) for graduate study, becoming a participant there in Maria Klawe's project on aphasia. She earned a master's degree at UBC in 2004, with the thesis ''Comparing Static, Adaptable, and Adaptive Menus'', and completed her Ph.D. in 2009 with the dissertation ''Supporting Feature Awareness and Improving Performance with Personalized Graphical User Interfaces'', both under the supervision of Joanna McGrenere. Career After postdoctoral research at the University of Washington with Professor Jacob O. Wobbrock, Findlater joined the College of Information Studies faculty, UMIACS, and University of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ad-hoc
Ad hoc is a Latin phrase meaning literally 'to this'. In English, it typically signifies a solution for a specific purpose, problem, or task rather than a generalized solution adaptable to collateral instances. (Compare with ''a priori''.) Common examples are ad hoc committees and commissions created at the national or international level for a specific task. In other fields, the term could refer to, for example, a military unit created under special circumstances (see '' task force''), a handcrafted network protocol (e.g., ad hoc network), a temporary banding together of geographically-linked franchise locations (of a given national brand) to issue advertising coupons, or a purpose-specific equation. Ad hoc can also be an adjective describing the temporary, provisional, or improvised methods to deal with a particular problem, the tendency of which has given rise to the noun ''adhocism''. Styling Style guides disagree on whether Latin phrases like ad hoc should be italicized. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ex-ante
The term ''ex-ante'' (sometimes written ''ex ante'' or ''exante'') is a phrase meaning "before the event". Ex-ante or notional demand refers to the desire for goods and services that is not backed by the ability to pay for those goods and services. This is also termed as 'wants of people'. ''Ex-ante'' is used most commonly in the commercial world, where results of a particular action, or series of actions, are forecast (or intended). The opposite of ''ex-ante'' is ''ex-post'' (actual) (or ''ex post''). Buying a lottery ticket loses you money ex ante (in expectation), but if you win, it was the right decision ex post. Examples: * In the financial world, the ''ex-ante return'' is the expected return of an investment portfolio. * In the recruitment industry, ''ex-ante'' is often used when forecasting resource requirements on large future projects. The ''ex-ante'' (and ''ex-post'') reasoning in economic topics was introduced mainly by Swedish economist Gunnar Myrdal in his 1927– ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
External Validity
External validity is the validity of applying the conclusions of a scientific study outside the context of that study. In other words, it is the extent to which the results of a study can be generalized to and across other situations, people, stimuli, and times.Aronson, E., Wilson, T. D., Akert, R. M., & Fehr, B. (2007). Social psychology. (4 ed.). Toronto, ON: Pearson Education. In contrast, internal validity is the validity of conclusions drawn ''within'' the context of a particular study. Because general conclusions are almost always a goal in research, external validity is an important property of any study. Mathematical analysis of external validity concerns a determination of whether generalization across heterogeneous populations is feasible, and devising statistical and computational methods that produce valid generalizations. Threats "A threat to external validity is an explanation of how you might be wrong in making a generalization from the findings of a particular study ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Internal Validity
Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a study can rule out alternative explanations for its findings (usually, sources of systematic error or 'bias'). It contrasts with external validity, the extent to which results can justify conclusions about other contexts (that is, the extent to which results can be generalized). Details Inferences are said to possess internal validity if a causal relationship between two variables is properly demonstrated.Shadish, W., Cook, T., and Campbell, D. (2002). Experimental and Quasi-Experimental Designs for Generilized Causal Inference Boston:Houghton Mifflin. A valid causal inference may be made when three criteria are satisfied: # the "cause" precedes the "eff ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Latent Dirichlet Allocation
In natural language processing, Latent Dirichlet Allocation (LDA) is a generative statistical model that explains a set of observations through unobserved groups, and each group explains why some parts of the data are similar. The LDA is an example of a topic model. In this, observations (e.g., words) are collected into documents, and each word's presence is attributable to one of the document's topics. Each document will contain a small number of topics. History In the context of population genetics, LDA was proposed by J. K. Pritchard, M. Stephens and P. Donnelly in 2000. LDA was applied in machine learning by David Blei, Andrew Ng and Michael I. Jordan in 2003. Overview Evolutionary biology and bio-medicine In evolutionary biology and bio-medicine, the model is used to detect the presence of structured genetic variation in a group of individuals. The model assumes that alleles carried by individuals under study have origin in various extant or past populations. The ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Topic Model
In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unsupervised Learning
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it. In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged. Neural networks Tasks vs. methods Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support Vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
