3,898 research outputs found
Towards Deep Semantic Analysis Of Hashtags
Hashtags are semantico-syntactic constructs used across various social
networking and microblogging platforms to enable users to start a topic
specific discussion or classify a post into a desired category. Segmenting and
linking the entities present within the hashtags could therefore help in better
understanding and extraction of information shared across the social media.
However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden),
the segmentation of hashtags into constituent entities ("NSA" and "Edward
Snowden" in this case) is not a trivial task. Most of the current
state-of-the-art social media analytics systems like Sentiment Analysis and
Entity Linking tend to either ignore hashtags, or treat them as a single word.
In this paper, we present a context aware approach to segment and link entities
in the hashtags to a knowledge base (KB) entry, based on the context within the
tweet. Our approach segments and links the entities in hashtags such that the
coherence between hashtag semantics and the tweet is maximized. To the best of
our knowledge, no existing study addresses the issue of linking entities in
hashtags for extracting semantic information. We evaluate our method on two
different datasets, and demonstrate the effectiveness of our technique in
improving the overall entity linking in tweets via additional semantic
information provided by segmenting and linking entities in a hashtag.Comment: To Appear in 37th European Conference on Information Retrieva
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
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