Project 2nd draft Derry Reyyan

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

Google Books Ngram Data.

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

For each of the ideas above, proposed techniques or related papers are (in order of the ideas):

• Regression analysis to measure tendency of a word to become negative in meaning over time, when co-occurred with negative words (Related paper: The Spread of Obesity in a Large Social Network over 32 Years - applied to measuring the spread of negativity in a network of words).

Evaluation

A combination of manual evaluation and cross validation (splitting the data into training and testing and evaluate) may be done.