Sentiment Analysis Using Machine Learning
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The document provides an overview of machine learning (ML), covering its definition, requirements, and components, distinguishing between supervised and unsupervised learning. It explains classification and regression, outlines various ML algorithms such as Naïve Bayes and Support Vector Machines, and discusses the efficiency of these techniques compared to traditional methods. The document also highlights the application of ML in sentiment analysis and includes references for further reading.
![Observations :
Ref: [1] Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY
18](https://image.slidesharecdn.com/ml-150410091325-conversion-gate01/85/Sentiment-Analysis-Using-Machine-Learning-18-320.jpg)
![[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.
Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY 20](https://image.slidesharecdn.com/ml-150410091325-conversion-gate01/85/Sentiment-Analysis-Using-Machine-Learning-20-320.jpg)
Sentiment Analysis Using Machine Learning
- 1. Nihar N Suryawanshi I.T Grad at University of Pune 1
- 2. 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
- 3. • 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. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 3
- 4. •Data •Pattern •Mathematical Representation Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 4
- 5. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 5
- 6. • 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. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 6
- 7. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 7
- 8. •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
- 9. • Naïve Bays • Support Vector Machines • Maximum Entropy Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 9
- 10. •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 Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 10
- 11. P(Y)=5/8=0.625 P(N)=3/8=0.375 Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 11
- 12. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 12 P(Chills=yes and flue =yes)= 3/5= 0.6
- 13. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 13
- 14. •Subject is divided into through Hyper plane which forms basis of classification •Designed by Vampik •Linear Classification Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 14
- 15. •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 Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 15
- 16. • To increase the efficiency we can combine traditional Lexicon based systems with Modern Classifier machines like Naïve Bayes or SVM. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 16
- 17. 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 Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 17
- 18. Observations : Ref: [1] Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 18
- 19. •The machine learning can prove efficient over traditional techniques for SA •The Naïve Bayes can be useful in sentiment analysis of text categorization. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 19
- 20. [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. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 20
- 21. -Nihar Suryawanshi. Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY 21