Twitter Sentiment Analysis-1 : AAP

Aim

Analyzing positive and negative sentiments of people towards newly emerged Aam Admi Party and Arvind Kejariwal before Oath Ceremony on 28th December 2013.

Method

To analyze public sentiment we have used Twitter as our test bed. On 8th December 2013 the results were announced and BJP emerged as single biggest party in the state with 32 seats however the party refused to form a government. Congress with its 8 MLAs gave an external support to AAP claiming to prevent Delhi from a fresh election. AAP was forced to form a Govt from both main stream parties on moral grounds. AAP announced to form government after 10 days of public referendum and public meetings and finally 6 MLAs took oath on 28^th Dec 2013 forming new government in Delhi. We used the Twitter API with Python interface to extract public tweets with a list of key word –

Aam admi party, AAP, Kejariwal, @AamAadmiParty, #AAP, #MyCMKejriwal, Kejriwal, @ArvindKejriwal, #Aap

We have trained a Naïve Bayes classifier over 22 negative sentiment tweets and 22 positive sentiment tweets. This classifier is then used to classify above mentioned tweets into positive and negative sentiment tweets.

Results

	#PositiveSentimentTweets	#NegativeSentimentTweets	%Positive
21 Dec 2013 - 28 Dec 2013	740	353	67.70

Limitations

1. Twitter is mostly used by high class, urban, celebrities and journalist communities and there the tweets collected are not a very accurate measure of social sentiment towards AAP. However it is a good starting point to evaluate social media sentiment towards AAP.
2. The analysis only used tweets in English language. Hindi and local languages haven’t been analyzed.
3.  Analysis of tweets is also limited by query terms used by us which only 9 terms found by us through manual tweet observations. 
4. Because of multiple Tags used for tweet filtering there are a few cases of duplicate tweets which can be both in positive and negative side and hasn't been analysed.
5. It is a binary classification with Positive and Negative classes. However, in practice some of the tweets may lie as neutral too.