Twitter Sentiments Analysis useful features

Question

I\'m trying to implement Sentiments Analysis functionality and looking for useful features which can be extracted from tweet messages.The features which I have in my mind for no

Answer 1

If I post really good news on twitter, a lot of people might start publicly congratulating me.
So If I post X, and then get a lot of 'Congrats' tweets from other people, then X is probably positive.
In general, the type and frequency of people who retweet my tweet might have something to do with its inherent sentiment.

Answer 2

Others that may be useful are:

• elongated words (eg. goooood)
• unigrams and bigrams of every word (particularly if you have a large corpus)

Regarding references: This tutorial by Christopher Potts is very good and to the point: http://sentiment.christopherpotts.net/

Other papers:

• Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Alexander Pak, Patrick Paroubek
• Twitter Sentiment Classification using Distant Supervision. Go et al. 2009.
• Robust Sentiment Detection on Twitter from Biased and Noisy Data. Barbosa and Feng. 2010.
• Sentiment strength detection in short informal text. Thelwall et al. (2010). JAIST

Answer 3

I would suggest the following articles:

• Belgian elections, June 13, 2010 - Twitter opinion mining, http://www.clips.ua.ac.be/pages/pattern-examples-elections
• 100 days of web mining, http://www.clips.ua.ac.be/pages/pattern-examples-100days