1. Performance Analysis of Supervised Machine
Learning Techniques for Sentiment Analysis
Presented By : BiswaRanja Samal
Software Engineer @ Acesocloud
IEEE 3rd International Conference on Sensing, Signal Processing & Security
Authors :
1. Anil Kumar Behera
2. Mrutyunjaya Panda
P.G. Department of Computer Science and Applications
UtkalUniversity,Vani Vihar,Bhubaneswar-751004, India

2. Contents
• Introduction
• Motivation
• Proposed Methodology
• Experimental Results
• Conclusions and Future Scope
• References

3. Introduction

4. >> What is Machine Learning?
>> Types of Machine Learning.
>>> Supervised Machine Learning
>>> Unsupervised Machine Learning
>>> Reinforcement Learning
>>> Transduction
>>> Semi-supervised Machine Learning

5. >> What is Sentiment Analysis?
>> Why Sentiment Analysis?
Expressing the emotions and feelings with the help of words
makes human beings unique[19]. These feelings are known as
the sentiments and the process of analyzing these statements is
known as the Sentiment Analysis.
Sentiment analysis along with the machine learning
techniques can result in the building of a high-performance
intelligent system and can proof its expertise in the area of
artificial intelligence [16].

6. Motivation

7. sometimes it becomes a very complex job for the researchers to
select an appropriate machine learning technique according to
their requirement which leads them to improper result with very
poor accuracy and performance of the model.
This motivated us towards doing an investigation on performance
analysis of available machine learning techniques for sentiment
analysis. We have considered only the supervised machine
learning techniques and have tried to do a comparison in each
criterion of this technique.

8. Proposed Methodology

9. Algorithm overflow of the proposed methodology
Step1 : Start
Step2 : for each dataset present in dataSetList
Step2.1: Clean the data set
Step2.2: Prepare training data set
Step2.3: Prepare testing data set
Step2.4: for each classifier present in classifierList
Step2.4.1: train classifier with training data set
Step2.4.2: test classifier with testing data set
Step2.4.3: Obtain the accuracy percent from result
Step3: Finish

10. Methodology in Details
>> Collecting Movie Review Data Sets
We have collected various size of movie review data sets such as
10600, 25000, 35600, 50000 and 85600.
>> Cleaning the Data Sets
Movie review data set consists of characters, numbers, special
characters and unrecognized characters. Which may create
hazard for our classifier, that’s why after collecting the data
sets we have undertaken the data set cleaning procedure
>> Data Categorization
Data Set Size Positive Feedbacks Negative Feedbacks
10,600 5,300 5,300
25,000 12,500 12,500
35,600 17,800 17,800
50,000 25,000 25,000
85,600 42,800 42,8001
Data sets showing numbers of positive and negative feedback

11. >> Preparing Training and Testing Data Sets
It’s a common convenience to use 70% of the data set for the
training purpose and use rest 30% of the data for testing the
model we have also followed it.
Data Set Size Training Data Set Size Testing Data Set Size
10,600 7420 3180
25,000 17500 7500
35,600 24920 10680
50,000 35000 15000
85,600 59918 25680
Representing number of training and testing reviews

12. >> Training the Model with Training Data Sets
>> Testing the Model with Testing Data Sets

13. Experimental Results

14. Representing used classifiers performance with highlighting the classifier
which has performed very accurately .

15. Representing the graphical representation of all classifiers performance .

16. Conclusions and Future Works

17. >> In this paper, a simple yet novel approach on sentiment
analysis of movie reviews is performed using seven
promising supervised machine learning algorithms.
>> The results obtained concludes linear SVC/SVM as the
best classifier among others in achieving 100% accuracy
for large number of movie reviews.
>> In future, we try to investigate its effectiveness
considering big datasets using the unsupervised and
semi supervised machine learning techniques.

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20. Thank You