Sentiment Analysis Using Machine Learning

Nihar N Suryawanshi
I.T Grad at University of Pune
1

Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY
2
1. What is ML
2. Requirements
3. Components of ML
4. Supervised VS Unsupervised
5. Classification VS Regression
6. Naïve Bayes
7. SVM
8. Maximum Entropy
9. Lexicon and Classifier
10.Comparison
11.Conclusion
12.References

• Machine learning is a type of artificial intelligence
(AI) that provides computers with the ability to
learn without being explicitly programmed.
• The Machine that Teaches Themselves.
3

•Data
•Pattern
•Mathematical Representation
4

5

• Supervised Learning:
In this type we provide essential information to
The machine. Input and Output Data sets are
provided
•Unsupervised Learning:
In this type not much info is provided and machine
gives results using tedious calculations.
6

7

•Classification means to group the output into a class.
•In Classification the output value is small and discrete.
Ex: tumor->yes or no.
•Regression means to predict the output value using training
data.(gives more detailed and approximate output).
•In Regression the output is continuous.
Ex: tumor ->harmful or not harmful. 8

• Naïve Bays
• Support Vector Machines
• Maximum Entropy
9

•Based on Bayesian theorem
•Bays theorem:
P(c | d) = P(c) P(d | c)
P(d)
c= event of Raining
d=event of Dark clouds
•We make assumption that Events are conditionally
independent
10

P(Y)=5/8=0.625 P(N)=3/8=0.375
11

12
P(Chills=yes and flue =yes)= 3/5= 0.6

13

•Subject is divided into through Hyper plane which forms
basis of classification
•Designed by Vampik
•Linear Classification
14

•Maximum Entropy is a Probability distribution estimation
Technique..
•The principal of Entropy is that without external knowledge
one should Prefer distribution that are uniform
•Here in probability events are Dependent
DEPERTMENT OF INFORMATION
TECHNOLOGY
15

• To increase the efficiency we can combine traditional Lexicon
based systems with Modern Classifier machines like
Naïve Bayes or SVM.
16

Naïve Bays SVM Maximum
Entropy
Easy to Implement Harder to
Implement
Harder to
Implement
Less Efficient,
Efficient due to
working with large
sets of Words
Efficiency is
maximum
Efficiency is
moderate
Limited Use Versatile
Used in Comp
Vision, Text Cat, IP
Hardly used
17

Observations :
Ref: [1] Sinhgad Academy Of Engineering, Pune
18

•The machine learning can prove efficient over traditional
techniques for SA
•The Naïve Bayes can be useful in sentiment analysis of text
categorization.
19

[1]Thumbsup?Sentiment Classiﬁcationusing Machine Learning Techniques.
BoPang and LillianLee,Shivakumar Vaithyanathan[IBM, Cornell University].
[2] Machine Learning Algorithms for Opinion Mining and Sentiment Classification
Jayashri Khairnar,Mayura Kinikar[IJSRP].
[3] An introduction to Machine Learning
Pierre Geurts[Department of EE and CS & Bioinformatics, University of Liège].
[4] A Tutorial on Naive Bayes Classification[Carnegie Mellon University ]
[5]Using Maximum Entropy for Text Classification[Carnegie Mellon University].
[6]combining Lexicon and leaning.[Andrius Mudinas][Dell Zhang]
[7] Wikipedia and Internet.
DEPERTMENT OF INFORMATION TECHNOLOGY 20

-Nihar Suryawanshi.
21

Sentiment Analysis Using Machine Learning

More Related Content

What's hot

Viewers also liked

Similar to Sentiment Analysis Using Machine Learning

Recently uploaded

Sentiment Analysis Using Machine Learning