The Resource Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce, by Zhaoyu Li

Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce, by Zhaoyu Li

Label
Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce
Title
Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce
Statement of responsibility
by Zhaoyu Li
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Data has been growing exponentially in recent years. With the development of information highway, data can be generated and collected very fast, and the data is so large that it has exceeded the limit of our conventional processing methods and applications. The social network is one of many data explosion areas. Among all social network medias, Twitter has become one of the most important platforms to share and communicate with friends. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document, and the sentiment analysis on Twitter has also been used as a valid indicator of stock prices in the past. Naive Bayes is an algorithm to perform sentiment analysis. MapReduce programming model provides a simple and powerful model to implement distributed applications without having deeper knowledge of parallel programming. When a new hypothetical MapReduce sentiment analysis system is built to provide certain performance goal, we are lack of the benchmark and the traditional trial-and-error solution is extremely time-consuming and costly. In this thesis we implemented a prototype system using Naive Bayes to find the correlation between the geographical sentiment on Twitter and the stock price behavior of companies. Also we implemented the Naive Bayes sentiment analysis algorithm in MapReduce model based on Hadoop, and evaluated the algorithm on large amount of Twitter data with different metrics. Based on the evaluation results, we provided a comprehensive MapReduce performance prediction model for Naive Bayes based sentiment analysis algorithm. The prediction model can predict task execution performance within a window, and can also be used by other MapReduce systems as a benchmark in order to improve the performance
Cataloging source
MUU
http://library.link/vocab/creatorName
Li, Zhaoyu
Degree
M.S.
Dissertation note
Thesis
Dissertation year
2014.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1967-
http://library.link/vocab/relatedWorkOrContributorName
Shang, Yi
http://library.link/vocab/subjectName
  • Data mining
  • Social media
  • Machine learning
Label
Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce, by Zhaoyu Li
Instantiates
Publication
Note
Dr. Yi Shang, Advisor
Bibliography note
Includes bibliographical references (pages 74-75)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
987303694
Extent
1 online resource (vii, 75 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)987303694
Label
Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce, by Zhaoyu Li
Publication
Note
Dr. Yi Shang, Advisor
Bibliography note
Includes bibliographical references (pages 74-75)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
987303694
Extent
1 online resource (vii, 75 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)987303694

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