Semantic hashtag relation classification using co-occurrence word information

Sungwon Seo; Jong-Kook Kim; Lynn Choi

doi:10.1109/ICUFN.2017.7993922

Semantic hashtag relation classification using co-occurrence word information

Sungwon Seo, Jong-Kook Kim, Lynn Choi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Social Networking Service users express their thoughts and feelings using hashtags. Hashtags can be related to other hashtags and these hashtags and images are used together in a post that the user wrote. Understanding the meaning of a hashtag is one of the ways to learn latent semantic expressions of words. Existing methods for learning semantic analysis use large corpus. This research focuses on the classification of semantic words using a user's hashtag data and co-occurrence hashtag information.

Original language	English
Title of host publication	ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	860-862
Number of pages	3
ISBN (Electronic)	9781509047499
DOIs	https://doi.org/10.1109/ICUFN.2017.7993922
Publication status	Published - 2017 Jul 26
Event	9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 - Milan, Italy Duration: 2017 Jul 4 → 2017 Jul 7

Other

Other	9th International Conference on Ubiquitous and Future Networks, ICUFN 2017
Country/Territory	Italy
City	Milan
Period	17/7/4 → 17/7/7

Keywords

Hashtag
Information Retrieval
Natural Language Processing
Social Networking Service

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICUFN.2017.7993922

Cite this

Seo, S, Kim, J-K & Choi, L 2017, Semantic hashtag relation classification using co-occurrence word information. in ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks., 7993922, IEEE Computer Society, pp. 860-862, 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017, Milan, Italy, 17/7/4. https://doi.org/10.1109/ICUFN.2017.7993922

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title = "Semantic hashtag relation classification using co-occurrence word information",

abstract = "Social Networking Service users express their thoughts and feelings using hashtags. Hashtags can be related to other hashtags and these hashtags and images are used together in a post that the user wrote. Understanding the meaning of a hashtag is one of the ways to learn latent semantic expressions of words. Existing methods for learning semantic analysis use large corpus. This research focuses on the classification of semantic words using a user's hashtag data and co-occurrence hashtag information.",

keywords = "Hashtag, Information Retrieval, Natural Language Processing, Social Networking Service",

author = "Sungwon Seo and Jong-Kook Kim and Lynn Choi",

year = "2017",

month = jul,

day = "26",

doi = "10.1109/ICUFN.2017.7993922",

language = "English",

pages = "860--862",

booktitle = "ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks",

publisher = "IEEE Computer Society",

note = "9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 ; Conference date: 04-07-2017 Through 07-07-2017",

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AU - Kim, Jong-Kook

AU - Choi, Lynn

PY - 2017/7/26

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N2 - Social Networking Service users express their thoughts and feelings using hashtags. Hashtags can be related to other hashtags and these hashtags and images are used together in a post that the user wrote. Understanding the meaning of a hashtag is one of the ways to learn latent semantic expressions of words. Existing methods for learning semantic analysis use large corpus. This research focuses on the classification of semantic words using a user's hashtag data and co-occurrence hashtag information.

AB - Social Networking Service users express their thoughts and feelings using hashtags. Hashtags can be related to other hashtags and these hashtags and images are used together in a post that the user wrote. Understanding the meaning of a hashtag is one of the ways to learn latent semantic expressions of words. Existing methods for learning semantic analysis use large corpus. This research focuses on the classification of semantic words using a user's hashtag data and co-occurrence hashtag information.

KW - Hashtag

KW - Information Retrieval

KW - Natural Language Processing

KW - Social Networking Service

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DO - 10.1109/ICUFN.2017.7993922

M3 - Conference contribution

AN - SCOPUS:85028045021

SP - 860

EP - 862

BT - ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

T2 - 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017

Y2 - 4 July 2017 through 7 July 2017

ER -

Semantic hashtag relation classification using co-occurrence word information

Abstract

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Keywords

ASJC Scopus subject areas

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