Difference between revisions of "OConnor et. al., ICWSM 2010"
| Line 8: | Line 8: | ||
== Summary == | == Summary == | ||
| − | This [[Category::Paper]] attempts | + | This [[Category::Paper]] attempts correlate the results of several surveys related consumer confidance and political opinions, with the sentiment words frequencies found in Twitter. The main motivation is that mining opinions in Twitter can be used as an alternative method to conducting surveys, which can be time consuming and comparatively expensive. |
| + | |||
| + | This task can be divided into two steps. First, collect relevant tweets from the Twitter corpora and then determine whether the tweets express positive or negative opinion. | ||
== Evaluation == | == Evaluation == | ||
Revision as of 10:03, 26 September 2012
Contents
Citation
Brendan O’Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010a. From tweets to polls: Linking text sentiment to public opinion time series. In Proc. of ICWSM.
Online version
From tweets to polls: Linking text sentiment to public opinion time series
Summary
This Paper attempts correlate the results of several surveys related consumer confidance and political opinions, with the sentiment words frequencies found in Twitter. The main motivation is that mining opinions in Twitter can be used as an alternative method to conducting surveys, which can be time consuming and comparatively expensive.
This task can be divided into two steps. First, collect relevant tweets from the Twitter corpora and then determine whether the tweets express positive or negative opinion.
