Abstract
Research in emotion analysis of text suggest
that emotion lexicon based features
are superior to corpus based n-gram features.
However the static nature of the
general purpose emotion lexicons make
them less suited to social media analysis,
where the need to adopt to changes in vocabulary
usage and context is crucial. In
this paper we propose a set of methods to
extract a word-emotion lexicon automatically
from an emotion labelled corpus of
tweets. Our results confirm that the features
derived from these lexicons outperform
the standard Bag-of-words features
when applied to an emotion classification
task. Furthermore, a comparative analysis
with both manually crafted lexicons and
a state-of-the-art lexicon generated using
Point-Wise Mutual Information, show that
the lexicons generated from the proposed
methods lead to significantly better classi-
fication performance.
Original language | English |
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Title of host publication | Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014) |
Publisher | *SEM 2014 Organising Committee |
Pages | 12-21 |
Number of pages | 10 |
ISBN (Print) | 9781941643259 |
Publication status | Published - 2014 |
Event | *SEM 2014 Third Joint Conference on Lexical and Computational Semantics - Ireland, Dublin, Ireland Duration: 23 Aug 2014 → 24 Aug 2014 |
Conference
Conference | *SEM 2014 Third Joint Conference on Lexical and Computational Semantics |
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Country/Territory | Ireland |
City | Dublin |
Period | 23/08/2014 → 24/08/2014 |