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An evaluation of machine learning algorithms for tweet sentiment analysis
Title
An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator
Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings
Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format
Document (PDF)
Intelligent systems using GMDH algorithms
Title
Intelligent systems using GMDH algorithms.
Creator
Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued
2010
PURL
http://purl.flvc.org/FAU/2976442
Subject Headings
GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format
Document (PDF)