SentimentAnalyzer

Simple sentiment analyzer that classifies tweets as either positive or negative.

Pipeline

Data Aquistion

From nltk.twitter_samples

Splitting

We have 10000 tweets, 5000 are Positive(+) and 5000 are Negative(-)
We will split to be 80% for training and 20% testing

Text Cleaning

for each tweet
	1- Remove stock market tickers
	2- Remove old retweet text "RT"
	3- Remove hyperlinks
	4- Remove hashtags 

PreProcessing(Ordered)

	1- Tokenization
	2- Remove stop words
	3- Remove punctuations
	4- Stemming

Output: list of processed words for each tweet.

Building Frequency Table for each list of words (word, class[+|-]): count

Exracting Features From Tweets

for each processed tweet
	tweet = (1, count of positive words, count of negative words)

Building X and Y (Real Dataset) X: tweet 1 (1, 10, 13) tweet 2 (1, 5, 11) tweet 3 (1, 9, 18) tweet 4 (1, 24, 13) ... and so on
Y: 1 (positive tweet) or 0 (negative tweet)

Modeling

Logisitic Regression
SVM 
Naive Bayes

Evaluation

Precision
Recall
F1 Score