54 research outputs found
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
Using Nuances of Emotion to Identify Personality
Past work on personality detection has shown that frequency of lexical
categories such as first person pronouns, past tense verbs, and sentiment words
have significant correlations with personality traits. In this paper, for the
first time, we show that fine affect (emotion) categories such as that of
excitement, guilt, yearning, and admiration are significant indicators of
personality. Additionally, we perform experiments to show that the gains
provided by the fine affect categories are not obtained by using coarse affect
categories alone or with specificity features alone. We employ these features
in five SVM classifiers for detecting five personality traits through essays.
We find that the use of fine emotion features leads to statistically
significant improvement over a competitive baseline, whereas the use of coarse
affect and specificity features does not.Comment: In Proceedings of the ICWSM Workshop on Computational Personality
Recognition, July 2013, Boston, US
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Rating scales are a widely used method for data annotation; however, they
present several challenges, such as difficulty in maintaining inter- and
intra-annotator consistency. Best-worst scaling (BWS) is an alternative method
of annotation that is claimed to produce high-quality annotations while keeping
the required number of annotations similar to that of rating scales. However,
the veracity of this claim has never been systematically established. Here for
the first time, we set up an experiment that directly compares the rating scale
method with BWS. We show that with the same total number of annotations, BWS
produces significantly more reliable results than the rating scale.Comment: In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), Vancouver, Canada, 201
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
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