Neural Fuzzy Inference Hybrid System with Support Vector Machine for Identification of False Singling in Stock Market Prediction for Profit Estimation

2020 ◽  
pp. 221-227
Author(s):  
Bhupinder Singh ◽  
Santosh Kumar Henge
Keyword(s):  
Support Vector Machine ◽  
Stock Market ◽  
Hybrid System ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Stock Market Prediction ◽  
For Profit ◽  
Neural Fuzzy

Indian Stock Market Prediction Based on Rough Set and Support Vector Machine Approach

2020 ◽  
pp. 345-355
Author(s):  
Rudra Kalyan Nayak ◽  
Ramamani Tripathy ◽  
Debahuti Mishra ◽  
Vijay Kumar Burugari ◽  
Prabha Selvaraj ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Stock Market ◽  
Rough Set ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Indian Stock Market

Sentiment Analysis

2018 ◽  
pp. 130-152 ◽  
Author(s):  
Syed Muzamil Basha ◽  
Dharmendra Singh Rajput
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Daily Activity ◽  
Opinion Mining ◽  
Fuzzy Inference ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Inference System ◽  
Neural Fuzzy

E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.

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