Results 1 - 10 of 196

TKLBLIIR: Detecting Twitter paraphrases with TweetingJay

by Mladen Karan, Bojana Dalbelo Bašić, Marie-francine Moens - In Proceedings of SemEval , 2015
"... When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi-larity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi-larity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model

H.: Semantic Smoothing for Twitter Sentiment Analysis

by Hassan Saif, Yulan He, Harith Alani - In: Proceeding of the 10th International Semantic Web Conference (ISWC) (2011
"... Abstract. Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character li ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
Abstract. Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character

Unitor: Combining syntactic and semantic kernels for twitter sentiment analysis

by Simone Filice, Danilo Croce, Roberto Basili - In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013 , 2013
"... In this paper, the UNITOR system participat-ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de-tection of a tweet is modeled as a classifica-tion task, tackled through a Multiple Kernel approach. It allows to combine the contribu-tion of complex kernel functions ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
In this paper, the UNITOR system participat-ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de-tection of a tweet is modeled as a classifica-tion task, tackled through a Multiple Kernel approach. It allows to combine the contribu-tion of complex kernel

Internal and External Evidence in the Identification and Semantic Categorization of Proper Names

by David D. Mcdonald - Corpus Processing for Lexical Acquisition , 1996
"... We describe the proper name recognition and classification facility ("PNF") of the SPARSER natural language understanding system. PNF has been used very successfully in the analysis of unrestricted texts in several sublanguages taken from online news sources. It makes its categorizations ..."
Abstract - Cited by 85 (0 self) - Add to MetaCart
categorizations on the basis of 'external' evidence from the context of the phrases adjacent to the name as well as 'internal' evidence within the sequence of words and characters. A semantic model of each name and its components is maintained and used for subsequent reference.

UKPDIPF: A Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data

by Lucie Flekova, Oliver Ferschke, Iryna Gurevych
"... We present a sentiment classification sys-tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit-ter. Our system expands tokens in a tweet with semantically similar expressions us-ing a large novel distributional thesaurus and calculates the semantic relatedness of the e ..."
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We present a sentiment classification sys-tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit-ter. Our system expands tokens in a tweet with semantically similar expressions us-ing a large novel distributional thesaurus and calculates the semantic relatedness

Alleviating Data Sparsity for Twitter Sentiment Analysis

by Hassan Saif, Yulan He, Harith Alani , 2012
"... Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-characte ..."
Abstract - Cited by 23 (6 self) - Add to MetaCart
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140

Engagement with Health Agencies on Twitter

by Sanmitra Bhattacharya, Padmini Srinivasan, Phil Polgreen, Sanmitra Bhattacharya, Padmini Srinivasan, Phil Polgreen
"... Objective: To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet. Methods: We c ..."
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, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups. Results: A third of the tweets (53,556) had zero retweets. Less than 1 % (613) had more than 100 retweets (mean

Sentiment Analysis in Twitter with Lightweight Discourse Analysis ABSTRACT

by Subhabrata Mukherjee, Pushpak Bhattacharyya
"... We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resources like parsing due to frequent failur ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
failure of the parsers to handle noisy data. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discourse particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate

KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter

by Hoang Long Nguyen, Trung Duc Nguyen, Dosam Hwang, Jason J. Jung
"... In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval-2015 Task 11. Particularly, the proposed model focuses on classifying the tweets into three categories (i.e., sarcastic, iro ..."
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In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval-2015 Task 11. Particularly, the proposed model focuses on classifying the tweets into three categories (i.e., sarcastic

SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)

by Wei Xu, Chris Callison-burch, William B. Dolan
"... In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) sys-tems for the Twitter data. Given a pair of sentences, participants are asked to produce a binary yes/no judgement or a graded score to measure their semantic equiv ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) sys-tems for the Twitter data. Given a pair of sentences, participants are asked to produce a binary yes/no judgement or a graded score to measure their semantic
Results 1 - 10 of 196