1. A Sentiment-Based Approach to Twitter User Recommendation BY AJAY ABDULPUR RAJARAM NIKKAM

2. Sentiment Analysis Algorithm Sentiment Analysis & Opinion Mining Entity is classified into 5 categories 1. Product 2.Person 3.Brand 4.Event 5.Concept

3. Goal of sentiment analysis system. The labels- Positive, Negative & Neutral Tweets Eg: emoticons (e.g: :-D ;-( ) hashtags (e.g: #iloveit, #ihate) keywords (e.g: good, sad) Salience

4. Multiple Granularity Levels Of Sentiment Analysis Feature Level Entity Level Sentence Level Document Level (Here we consider Sentiment Analysis as Sentence Level)

5. Machine Learning Algorithm A Naïve’s Bayes classifier is trained on the training data, where each tweet is represented as a feature vector made up of the following groups of features: Bag-of-words Word polarities Negations Elongated words Part-of-speech tags

6. SVO Recommendation Approach User Profiling

7. SVO Weighting Function - Idea behind the work. - First contribution S(u,c) - Second Contribution