User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis

Algorithm 1

Tweet sentiment analysis.

Input: tweet description, positive keywords, and negative keywords
Steps
(1)	Obtain the tweets related to registered companies
(2)	Measure the count of the tweets
(3)	For each of the tweet
(a)	Obtain the tweet description
(b)	Convert the description into a set of statements using a delimiter
(c)	Measure the sentiment score for each statement based on the probability of positive or negative sentiment based on words of specific sentiment class
(d)	If the positive sentiment score is higher than or equal to the negative sentiment, then the positive sentiment is incremented by a factor of 1, otherwise negative sentiment is increment
(e)	Like this, the total sentiment for the entire tweet is determined by doing a summation from all statements
(4)	For each tweets, the storage is done based on a total score of sentiment in the format of {TweetId, Positive Sentiment, Negative Sentiment, Company Name}.