73,511 research outputs found
Investigating redundancy in emoji use : study on a twitter based corpus
In this paper we present an annotated corpus created with the aim of analyzing the informative behaviour of emoji – an issue of importance for sentiment analysis and natural language processing. The corpus consists of 2475 tweets all containing at least one emoji, which has been annotated using one of the three possible classes: Redundant, Non Redundant, and Non Redundant + POS. We explain how the corpus was collected, describe the annotation procedure and the interface developed for the task. We provide an analysis of the corpus, considering also possible predictive features, discuss the problematic aspects of the annotation, and suggest future improvements.peer-reviewe
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
A study of inter-annotator agreement for opinion retrieval
Evaluation of sentiment analysis, like large-scale IR evalu-
ation, relies on the accuracy of human assessors to create
judgments. Subjectivity in judgments is a problem for rel-
evance assessment and even more so in the case of senti-
ment annotations. In this study we examine the degree to
which assessors agree upon sentence-level sentiment anno-
tation. We show that inter-assessor agreement is not con-
tingent on document length or frequency of sentiment but
correlates positively with automated opinion retrieval per-
formance. We also examine the individual annotation cate-
gories to determine which categories pose most di±culty for
annotators
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology
Ideological leanings of an individual can often be gauged by the sentiment
one expresses about different issues. We propose a simple framework that
represents a political ideology as a distribution of sentiment polarities
towards a set of topics. This representation can then be used to detect
ideological leanings of documents (speeches, news articles, etc.) based on the
sentiments expressed towards different topics. Experiments performed using a
widely used dataset show the promise of our proposed approach that achieves
comparable performance to other methods despite being much simpler and more
interpretable.Comment: Presented at EMNLP Workshop on Computational Approaches to
Subjectivity, Sentiment & Social Media Analysis, 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
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