516 research outputs found
Comparing and Combining Sentiment Analysis Methods
Several messages express opinions about events, products, and services,
political views or even their author's emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.Comment: Proceedings of the first ACM conference on Online social networks
(2013) 27-3
Analyzing the Targets of Hate in Online Social Media
Social media systems allow Internet users a congenial platform to freely
express their thoughts and opinions. Although this property represents
incredible and unique communication opportunities, it also brings along
important challenges. Online hate speech is an archetypal example of such
challenges. Despite its magnitude and scale, there is a significant gap in
understanding the nature of hate speech on social media. In this paper, we
provide the first of a kind systematic large scale measurement study of the
main targets of hate speech in online social media. To do that, we gather
traces from two social media systems: Whisper and Twitter. We then develop and
validate a methodology to identify hate speech on both these systems. Our
results identify online hate speech forms and offer a broader understanding of
the phenomenon, providing directions for prevention and detection approaches.Comment: Short paper, 4 pages, 4 table
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