Title

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.

This document is currently not available here.

Share

COinS