OConnor et. al., ICWSM 2010

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.

Polls

Public opinion polls considered in this work are obtained by telephone surveys and available to the public.

For comsumer confidence the survey from the University of Michigan was used (available here)

Aggregate Sentiment

This paper shows that simple methods that are subject to noise perform relatively well when used to estimate aggregate sentiment.

Identifying related tweets is performed simply by searching tweets that contain a set of pre-selected keywords, such as Obama and Mccain for tweets about presidential elections. The general sentiment of a tweet is identified by looking for words that have an either positive or negative polarity, and the sentiment of a tweet is classified as positive if it contains a positive polarity word and negative if it contains a negative polarity word or both if it contains positive and negative polarity words. The aggregate opinion is calculated as the ratio between the number of positive tweets and the number of negative ones.

We can see that in both steps, the methods leave a large margin of error. Firstly, there is no guarantee that all messages with the keywords will be related to the topic of interest. Furthermore, the method for classifing the tweets as positive and negative, without looking at the context, is a very basic one in literature.

However, since the goal is to estimate the aggregate opinion, the noise in each individual tweet can be amortized by having a significant sample of tweets.