Title of the Project: Election Prediction

About the project: Social networking sites have provided a huge platform for interaction among the crowd world-wide, Twitter being one of those. The large audience on twitter is responsible for tons of tweeting happening everyday i.e. sharing their view in relatively fewer words and hence providing researchers a large pool of tweets, which may contain anger or love towards an entity like an election. Using the concept of sentiment analysis we can extract their sentiments from these tweets and use these in predicting the outcome of any event, be it elections in our case. Thus we are bringing the concept of sentiment analysis together with twitter data, which would contain tweets and hash-tags, so as to predict the elections result. Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative.

We would be performing the following steps:-

• Data fetching from twitter API (TWEEPY)

• Cleaning the data fetched and bringing it to its simplest form

• Feature extraction

• Model training (making the machine understand the sentiment behind any tweet).

• Selection of most efficient algorithm to obtain correct results.

• Post processing (Sentiment Analysis : word level and sentence level).

• Obtaining results (In form of graphs and statistics).