Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2014

Abstract

As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this paper, we propose a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 26th ACM Conference on Hypertext and Social Media (Hypertext 2015)

Volume

3

First Page

283

Last Page

292

ISBN

978-981-3223-60-8

Identifier

10.1145/2700171.2791035

Publisher

ACM Press

City or Country

Guzelyurt, North Cyprus

Additional URL

https://doi.org/10.1145/2700171.2791035

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