Recognizing Complex Negation on Twitter
نویسندگان
چکیده
After the Great East Japan Earthquake in 2011, an abundance of false rumors were disseminated on Twitter that actually hindered rescue activities. This work presents a method for recognizing the negation of predicates on Twitter to find Japanese tweets that refute false rumors. We assume that the predicate “occur” is negated in the sentence “The guy who tweeted that a nuclear explosion occurred has watched too many SF movies.” The challenge is in the treatment of such complex negation. We have to recognize a wide range of complex negation expressions such as “it is theoretically impossible that...” and “The guy who... watched too many SF movies.” We tackle this problem using a combination of a supervised classifier and clusters of n-grams derived from large un-annotated corpora. The n-gram clusters give us a gain of about 22% in F-score for complex negations.
منابع مشابه
Negation Scope Detection for Twitter Sentiment Analysis
The paper describes the first sophisticated negation scope detection system for Twitter sentiment analysis. The system has been evaluated both on existing corpora from other domains and on a corpus of English Twitter data (tweets) annotated for negation. It produces better results than what has been reported in other domains and improves the performance on tweets containing negation when incorp...
متن کاملNegation, Contrast and Contradiction in Text Processing
This paper describes a framework for recognizing contradictions between multiple text sources by relying on three forms of linguistic information: (a) negation; (b) antonymy; and (c) semantic and pragmatic information associated with the discourse relations. Two views of contradictions are considered, in which a novel method of recognizing contrast and of finding antonymies are described. Contr...
متن کاملPredicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory
In this paper, we introduce Long ShortTerm Memory (LSTM) recurrent network for twitter sentiment prediction. With the help of gates and constant error carousels in the memory block structure, the model could handle interactions between words through a flexible compositional function. Experiments on a public noisy labelled data show that our model outperforms several feature-engineering approach...
متن کاملWhat Are Tweeters Doing: Recognizing Speech Acts in Twitter
Speech acts provide good insights into the communicative behavior of tweeters on Twitter. This paper is mainly concerned with speech act recognition in Twitter as a multiclass classification problem, for which we propose a set of word-based and character-based features. Inexpensive, robust and efficient, our method achieves an average F1 score of nearly 0.7 with the existence of much noise in o...
متن کاملA High-Performance Model based on Ensembles for Twitter Sentiment Classification
Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment ta...
متن کامل