Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/296823
Title: Certain investigation on predicting the users behavior in social networks using enhanced graph based semi supervised learning algorithm
Researcher: Balaanand M
Guide(s): Karthikeyan N
Keywords: Engineering and Technology
Computer Science
Computer Science Information Systems
Users behavior
Social networks
Hybrid Context Classifier
University: Anna University
Completed Date: 2019
Abstract: newline newline Social media offers a flexible environment where user can share their thoughts and feelings via text emoji s Images etc The volume velocity and Veracity of the data generated are huge high speed and with the uncertainty of accuracy Big Data is a kind of data with colossal Volume Variety and a high Velocity of data cohort The data generated through high Velocity does not support a modest mechanism to capture and process the information In order to capture the streaming data and utilize the information for prediction of user behavioural analysis is challenging Clustering and classification for streaming data are immensely complicated and results in an incorrect prediction Hence this complication results in a massive loss in the Healthcare sector concerning the prediction of suitable medicine for the appropriate disease Also, in other sectors it results in massive loss in optimized decision making for a natural disaster by government organization bandwidth allocation in social networking sites and manufacturing the products in industries etc Hence Sentimental Analysis or Opinion Mining is used to regulate the detection of subjective data such as opinions attitudes emotions and feelings Sentimental Analysis is an area of enormous potential where machines can learn or train by algorithms which makes better decision comparatively while the techniques of Sentimental Analysis can vary from very simple nuanced to complicate nuanced therefore existing research focus only on binary sentiment classification Since the data generated is enormous and with high speed Apache Hadoop is used for storing through Hadoop Distributed File System HDFS and processing through MapReduce Hadoop offers the storage of the data in replication to avoid data failures The MapReduce framework is a combination of two sections namely the Map and Reduce Primarily the input data could be segmented into a various section using replica key value pairs Apache Flume isa progressively reliable distributed and configurable tool
Pagination: xxii, 183p.
URI: http://hdl.handle.net/10603/296823
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File49.33 kBAdobe PDFView/Open
02_certificates.pdf922.25 kBAdobe PDFView/Open
03_abstracts.pdf39.38 kBAdobe PDFView/Open
04_acknowledgements.pdf920.52 kBAdobe PDFView/Open
05_contents.pdf45.99 kBAdobe PDFView/Open
06_listoftables.pdf22.91 kBAdobe PDFView/Open
07_listoffigures.pdf30.41 kBAdobe PDFView/Open
08_listofabbreviations.pdf33.66 kBAdobe PDFView/Open
09_chapter1.pdf197.3 kBAdobe PDFView/Open
10_chapter2.pdf157.38 kBAdobe PDFView/Open
11_chapter3.pdf312.99 kBAdobe PDFView/Open
12_chapter4.pdf511.64 kBAdobe PDFView/Open
13_chapter5.pdf431.06 kBAdobe PDFView/Open
14_conclusion.pdf27.97 kBAdobe PDFView/Open
15_references.pdf108.73 kBAdobe PDFView/Open
16_listofpublications.pdf56.51 kBAdobe PDFView/Open
80_recommendation.pdf60.24 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: