3,770 research outputs found
Towards Syntactic Iberian Polarity Classification
Lexicon-based methods using syntactic rules for polarity classification rely
on parsers that are dependent on the language and on treebank guidelines. Thus,
rules are also dependent and require adaptation, especially in multilingual
scenarios. We tackle this challenge in the context of the Iberian Peninsula,
releasing the first symbolic syntax-based Iberian system with rules shared
across five official languages: Basque, Catalan, Galician, Portuguese and
Spanish. The model is made available.Comment: 7 pages, 5 tables. Contribution to the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2017)
at EMNLP 201
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Negators, modals, and degree adverbs can significantly affect the sentiment
of the words they modify. Often, their impact is modeled with simple
heuristics; although, recent work has shown that such heuristics do not capture
the true sentiment of multi-word phrases. We created a dataset of phrases that
include various negators, modals, and degree adverbs, as well as their
combinations. Both the phrases and their constituent content words were
annotated with real-valued scores of sentiment association. Using phrasal terms
in the created dataset, we analyze the impact of individual modifiers and the
average effect of the groups of modifiers on overall sentiment. We find that
the effect of modifiers varies substantially among the members of the same
group. Furthermore, each individual modifier can affect sentiment words in
different ways. Therefore, solutions based on statistical learning seem more
promising than fixed hand-crafted rules on the task of automatic sentiment
prediction.Comment: In Proceedings of the 7th Workshop on Computational Approaches to
Subjectivity, Sentiment and Social Media Analysis (WASSA), San Diego,
California, 201
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
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