Discovering trends in twitter data
Date of Publication
2014
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Charibeth K. Cheng
Defense Panel Member
Charibeth K. Cheng
Ethel C. Ong
Ralph Vincent J. Regalado
Allan B. Borra
Abstract/Summary
Twitter is one of the social media streams that has contributed to the alteration of the way people communicate and interact with one another. The one concept that makes Twitter what it is, is the concept of trends. Trends are topics that are immediately popular at a certain period of time. End-users evidently applied the use of Twitter trends to various fields such as business, marketing, politics, news reporting, weather, and the like. This paper discusses the implementation of Woodpecker, a trend detection that harnesses both a language modeler and topic modeler to detect trends from Twitter data. Furthermore, Woodpecker enables its users to dig deeper into trends by drilling down the trends that it is able to detect. These outputs are then displayed in various forms of visualization.
Abstract Format
html
Language
English
Format
Electronic
Accession Number
CDTU019257
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
leaves ; 4 3/4 in.
Recommended Citation
Chua, G., Naval, N., & Rapes, M. (2014). Discovering trends in twitter data. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/10941