Computational methods frequently used in Analysis of Social Media
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These are some computational methods discussed in Social Media Analysis 10-802 in Spring 2010.
- k-means clustering
- a way to cluster items so that there are items within a cluster are close, and distances between clusters are larger. K-means clustering is sometimes used as a subroutine in spectral clustering. See the Class Meeting for 10-802 02/04/2010
- Naive Bayes classifier learning
- A fast and simple way to learn a classifier, especially useful for text. See the Class Meeting for 10-802 01/14/2010
- pointwise mutual information (PMI)
- a particular way of measuring correlation between two items, usually words or phrases in text. Comparing the PMI of a word w to positive terms (e.g., "excellent") to the PMI of word w to negative terms (e.g., "poor") is a good indicator of semantic orientation, if a large corpus is used. See the Class Meeting for 10-802 01/14/2010
- spectral clustering
- a way to cluster nodes in a graph into "blocks" so that there are many connections within blocks and fewer between blocks, based on clustering in a space defined by the eigenvalues of the graph Laplacian. This is a way of solving a graph cut problem. See the Class Meeting for 10-802 02/04/2010
- support vector machine classifier learning (SVMs)
- A widely-used way to learn a classifier, especially useful for text. See the Class Meeting for 10-802 01/14/2010
- Topic model
- A widely-used way to learn the underlying semantic structure of a collection of documents. See the Class Meeting for 10-802 02/16/2010
- Relational topic model
- A specific topic model that allows links between documents to be modeled. See the Class Meeting for 10-802 02/18/2010
- Random walk with restart
- A technique to find relavance score between any two nodes in a weighted graph
- A property of cost functions that explains the utility of greedy methods and speeds up their performance when used on cost functions that have this trait.
- Logistic regression
- A technique used for prediction of the probability of occurrence of an event by fitting data to a logistic curve.