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Item

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  1. University of Information Technology
  2. Faculty of Information Science

Semi-supervised Event Message Identification System for Targeted Domain

http://hdl.handle.net/20.500.12678/0000005355
http://hdl.handle.net/20.500.12678/0000005355
5e813c26-8191-4da2-87a4-6f1527128cfe
8d67f194-84c7-462b-9faa-790cdb72f791
Publication type
Conference paper
Upload type
Publication
Title
Title Semi-supervised Event Message Identification System for Targeted Domain
Language en
Publication date 2018-10-11
Authors
San San Nwe
Nang Saing Moon Kham
Description
Social media have become increasingly popular
components of our everyday lives in today’s globalizing society.
They provide a context where people across the world can
communicate, exchange messages, share knowledge, and
interact with each other regardless of the distance that
separates them. This research trend, extraction of events for
specific domain from these social media is emerging speedily
ranging from business intelligence to nation security field. The
short length of Twitter messages and frequent use of informal
and ungrammatical language challenge many long standing
approaches for automatically detecting and categorizing events
using streamed data in Event Message Identification system. A
semi-supervised approach with Support Vector Machine (SVM)
in combination with the corpus to identify the events from
twitter for targeted domain in specific location is proposed in
this paper. The experimental results show that the proposed
semi-supervised SVM model is more efficient than a strong
state-of-the-art semi-supervised classification model of Logic
Regression, Naïve Bayes and Decision Tree.
Keywords
Social media, Twitter, Semi-supervised, Events, SVM
Conference papers
IEEE, ICTT
8-11 October, 2018
18th IEEE International Conference on Communication Technology
China
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