Analysis of Sentiment in Twitter
Speaker: Michael Salter-Townshend (UCD)
Date: Fri 25th February 2011
Location: Statistics Seminar Room- L550 Library building
This is early work on the analysis of sentiment in Twitter data. We have a large volume (millions of tweets) from twitter.com and the goal is to analyse the sentiment contained in the tweets. We have also manually labelled a small subset of commonly occurring words and smileys as relating to either positive or negative sentiment.
We model the number of positive words in a tweet as Binomially distributed with mean equal to the number of words in the tweet times the tweet sentiment. We employ an EM algorithm where the E step calculates the expected sentiment label for the unlabelled words appearing in the tweets and the M step nds maximum-likelihood estimates for the sentiment in each tweet.
We present preliminary results and would like to discuss challenges, choices and extensions to the work.
(This talk is part of the Working Group on Statistical Learning series.)