Difference between revisions of "Project 2nd draft Derry Reyyan"
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(1) [[UsesMethod::Linear regression]] analysis to measure tendency of a word to become negative/positive in meaning over time, when co-occurred with negative/positive words | (1) [[UsesMethod::Linear regression]] analysis to measure tendency of a word to become negative/positive in meaning over time, when co-occurred with negative/positive words | ||
| − | (Related paper: [http://www.nejm.org/doi/pdf/10.1056/NEJMsa066082 | + | (Related paper: [[RelatedPaper::Nicholas A. Christakis, M.D., Ph.D., M.P.H., and James H. Fowler, Ph.D. (2007) The Spread of Obesity in a Large Social Network over 32 Years]]: [http://www.nejm.org/doi/pdf/10.1056/NEJMsa066082 External Link] - techniques applied to spread of obesity in a network of people). |
(2) Identify break points in the states of an entity (based on its co-occurrence changes) and find events that correspond to the break points | (2) Identify break points in the states of an entity (based on its co-occurrence changes) and find events that correspond to the break points | ||
| − | (Related paper: [http://snap.stanford.edu/soma2010/papers/soma2010_9.pdf | + | (Related paper: [[RelatedPaper::Akcora et.al. (2010) Identifying Breakpoints in Public Opinion]]: [http://snap.stanford.edu/soma2010/papers/soma2010_9.pdf External Link] - technique to identify break points in sentiment found in tweets (Twitter), using a set of manually constructed emotion words ([[UsesMethod::Vector space models]])) |
| − | (Related paper: [http://www.sciencemag.org/content/early/2010/12/15/science.1199644 | + | (Related paper: [[RelatedPaper:Michel et.al. (2010) Quantitative Analysis of Culture Using Millions of Digitized Books]]: [http://www.sciencemag.org/content/early/2010/12/15/science.1199644 External Link] - measure usage frequency over time of a given n-gram (such as "slavery", "great war", etc) that represents an entity of interest) |
(3) Graph modeling of co-occurrences (where nodes are words and edges are weighted by number of co-occurrences between words). Use of techniques from dynamic network evolution, link analysis or clustering to model and analyze changes in this graph over time | (3) Graph modeling of co-occurrences (where nodes are words and edges are weighted by number of co-occurrences between words). Use of techniques from dynamic network evolution, link analysis or clustering to model and analyze changes in this graph over time | ||
| − | (Related paper: [http://www.cs.rpi.edu/research/pdf/08-08.pdf | + | (Related paper: [[RelatedPaper::Cemal Cagatay Bilgin and Bülent Yener (2010) Dynamic Network Evolution: Models, Clustering, Anomaly Detection]]: [http://www.cs.rpi.edu/research/pdf/08-08.pdf External Link]) |
== Evaluation == | == Evaluation == | ||
A combination of manual evaluation and cross validation (splitting the data into training and testing and evaluate) may be done. | A combination of manual evaluation and cross validation (splitting the data into training and testing and evaluate) may be done. | ||
Revision as of 19:32, 14 February 2011
Social Media Analysis Project Ideas
Team Members
Derry Wijaya [dwijaya@cs.cmu.edu]
Reyyan Yeniterzi [reyyan@cs.cmu.edu]
Project Idea
Understanding change
Given an entity of interest, we would like to model and analyze its change in terms of words and phrases that co-occur with it over time.
We propose to construct a social graph, but instead of people, we put words as nodes and edges are weighted based on number of co-occurrence between the words. Using this social graph of words, we propose to analyze:
(1) how co-occurrence with other words change over time
(2) how the change influences the state (semantic or sentiment) associated with the entity
(3) how the change may correspond to events that occur during the same period of time
For example, the entity 'BP' frequently co-occurred with negatively associated words during and after the Gulf-spill event.
Dataset
Motivation
The co-occurrence of words changes over time
It will be interesting to model this change and analyze:
(1) How the state (semantic or sentiment) of a given entity changes over time depending on its neighbors (i.e. co-occurring words/phrases)
(2) How such changes relate to events that occur in the same period of time
(3) Whether we can find a natural sequence of events that define a change of state (semantic or sentiment) of a given entity
(4) Whether we can use (3) to predict the change of state of a given entity
Techniques
(1) Linear regression analysis to measure tendency of a word to become negative/positive in meaning over time, when co-occurred with negative/positive words
(Related paper: Nicholas A. Christakis, M.D., Ph.D., M.P.H., and James H. Fowler, Ph.D. (2007) The Spread of Obesity in a Large Social Network over 32 Years: External Link - techniques applied to spread of obesity in a network of people).
(2) Identify break points in the states of an entity (based on its co-occurrence changes) and find events that correspond to the break points
(Related paper: Akcora et.al. (2010) Identifying Breakpoints in Public Opinion: External Link - technique to identify break points in sentiment found in tweets (Twitter), using a set of manually constructed emotion words (Vector space models))
(Related paper: RelatedPaper:Michel et.al. (2010) Quantitative Analysis of Culture Using Millions of Digitized Books: External Link - measure usage frequency over time of a given n-gram (such as "slavery", "great war", etc) that represents an entity of interest)
(3) Graph modeling of co-occurrences (where nodes are words and edges are weighted by number of co-occurrences between words). Use of techniques from dynamic network evolution, link analysis or clustering to model and analyze changes in this graph over time
(Related paper: Cemal Cagatay Bilgin and Bülent Yener (2010) Dynamic Network Evolution: Models, Clustering, Anomaly Detection: External Link)
Evaluation
A combination of manual evaluation and cross validation (splitting the data into training and testing and evaluate) may be done.
