Sentiment Analysis
Sentiment Analysis
Sentiment Analysis
SUBMITTED BY
Himesh S Kulshrestha Chirag Bansal
00751202713 01451202713
Training Testing
OBJECTIVE
Our work involves performing sentiment analysis on live twitter data i.e
real time data, which we gather from the Twitter website using Tweepy
(an API), using various Machine Learning algorithms like Naïve Bayes
and its variants, Support Vector Clustering and Logistical Regression
after performing the classification, chunking, and tagging the part of
speech using Natural Language Processing.
NATURAL LANGUAGE PROCESSING TECHNIQUES
1.TOKENIZING
It is the process of breaking a stream of text up into the words, phrases or symbols called
tokens.
2.STOP WORDS
One of the major forms of pre processing is to filter useless data and in natural language
processing useless data are referred to as stop words.
4.CHUNKING
One of the major goals of chunking is to group into what are known as noun phrases.
NATURAL LANGUAGE PROCESSING TECHNIQUES
6. LEMMATIZATION
Stemming can often create non-existent words, whereas lemmas are actual words.
• NAIVE BAYES
The algorithm that we're going to use first is the Naïve Bayes. This
is a pretty popular algorithm used in text classification, so it is only
fitting that we try it out first. Before we can train and test our
algorithm, however, we need to go ahead and split up the data into
a training set and a testing set.
MACHINE LEARNING ALGORITHMS
LOGISTIC REGRESSION