Sara Rosenthal

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This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a twopoint and on a five-point ordinal scale, and quantification of the distribution(More)
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous(More)
It has long been established that there is a correlation between the dialog behavior of a participant and how influential he or she is perceived to be by other discourse participants. In this paper we explore the characteristics of communication that make someone an opinion leader and develop a machine learning based approach for the automatic(More)
Determining when conversational participants agree or disagree is instrumental for broader conversational analysis; it is necessary, for example, in deciding when a group has reached consensus. In this paper, we describe three main contributions. We show how different aspects of conversational structure can be used to detect agreement and disagreement in(More)
This paper explores the task of building an accurate prepositional phrase attachment corpus for new genres while avoiding a large investment in terms of time and money by crowdsourcing judgments. We develop and present a system to extract prepositional phrases and their potential attachments from ungrammatical and informal sentences and pose the subsequent(More)
This paper explores the automatic detection of sentences that are opinionated claims, in which the author expresses a belief. We use a machine learning based approach, investigating the impact of features such as sentiment and the output of a system that determines committed belief. We train and test our approach on social media, where people often try to(More)
We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads. This is the first agreement corpus to offer full-document annotations for threaded discussions. We provide a methodology for coding responses as well as an implemented tool with an interface that facilitates annotation of a specific(More)
Sentence fusion enables summarization and question-answering systems to produce output by combining fully formed phrases from different sentences. Yet there is little data that can be used to develop and evaluate fusion techniques. In this paper, we present a methodology for collecting fusions of similar sentence pairs using Amazon’s Mechanical Turk,(More)
We investigate whether wording, stylistic choices, and online behavior can be used to predict the age category of blog authors. Our hypothesis is that significant changes in writing style distinguish pre-social media bloggers from post-social media bloggers. Through experimentation with a range of years, we found that the birth dates of students in college(More)
We present the development and evaluation of a semantic analysis task that lies at the intersection of two very trendy lines of research in contemporary computational linguistics: (i) sentiment analysis, and (ii) natural language processing of social media text. The task was part of SemEval, the International Workshop on Semantic Evaluation, a semantic(More)