View source for Problems frequently addressed in Analysis of Social Media

These are some technical problems discussed in [[Social Media Analysis 10-802 in Spring 2010]].

; fitting parameters of a random graph model
: Since random graph models have parameters (e.g., the mean degree parameter for an Erdos-Renyi graph), this leads to the problem of finding the best values of these parameters, given a particular graph.  Depending on the complexity of the graph model this can be simple or complex. See the [[Class Meeting for 10-802 01/28/2010]]

; identifying [[the semantic orientation of words]]
: In sentiment analysis, the problem of automatically classifying words or phrases (often, just adjectives) as typically "thumbs up" or "thumbs down" (of course sometimes the actual orientation is context-dependent). See the [[Class Meeting for 10-802 01/14/2010]]

; mincut problem for a graph
: The problem of finding a small set of edges to delete from a graph in order to "best" break it into 2 (or sometimes more) components.  There are a number of variants of this problem, which make slightly different assumptions about the kind of links in the graph and/or different choices about how to measure the "best" cut.  See the [[Class Meeting for 10-802 02/04/2010]]

; normalized cut or NCut problem
: A variant of the graph cut problem.  See the [[Class Meeting for 10-802 02/04/2010]]

; opinion question-answer
: In sentiment analysis, the problem of automatically answering questions about opinions (private states) based on analysis of text.  See the [[Class Meeting for 10-802 01/14/2010]]

; product review mining
: In sentiment analysis, the problem of automatically preprocessing product reviews to facilitate shopping.  See the [[Class Meeting for 10-802 01/14/2010]]

; ratio cut problem
: A variant of the graph cut problem.  See the [[Class Meeting for 10-802 02/04/2010]]

; [[review classification]]
: In sentiment analysis, the problem of automatically classifying product reviews, usually as "thumbs up" or "thumbs down". See the[[Class Meeting for 10-802 01/14/2010]]


; [[tag recommendation]]
: Given a resource (photo, link, etc.) and tags assigned to this resource, recommend tags to enrich the annotation.

; word sense disambiguation
: Given a word's appearance in context, the problem of automatically determining which sense of a word is intended. See the[[Class Meeting for 10-802 01/14/2010]] for a discussion of this problem as it relates to finding the semantic orientation of words.

; neighbourhood formation(NF) and anomaly detection(AD)
: NF refers to finding out close neighbours of a node in large social graph. AD refers to finding the nodes which connects nodes in different neighborhoods. See the [[Neighborhood formation and Anomaly detection]] for discussion of the methods to solve these problems on bipartite graphs.

; Correlation between various network attributes
: This refers to finding out which attributes are related to which other attributes and how strong the dependence is. A widely used method for this task is finding out [[Pearson correlation coefficient]].

; [[Community Detection]]
: In network we often find nodes whose mutual interaction is stronger with some nodes than  the rest of the network. To find groups of tightly interactive nodes is commonly known as community detection.