ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

نویسندگان

  • Delia Irazú Hernández Farías
  • Emilio Sulis
  • Viviana Patti
  • Giancarlo Ruffo
  • Cristina Bosco
چکیده

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only wordsentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets...

متن کامل

LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets

In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at identifying the sentiment intensity of figurative language in tweets. We use various features, including those specially concerned with the identification of irony and sarcasm. The features are evaluated through a decision tree regression model and a support vector regression model. The experiment result of...

متن کامل

LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for c...

متن کامل

Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets

Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the expressiveness. This task becomes even more difficult when people use figurative language (e.g. irony, sarcasm and metaphors) because it causes a mismatch between the literal meaning and the actual expressed sentiment. In this paper, we describe a sentiment analysis system...

متن کامل

An Empirical, Quantitative Analysis of the Differences Between Sarcasm and Irony

A variety of classification approaches for the detection of ironic or sarcastic messages has been proposed in the last decade to improve sentiment classification. However, despite the availability of psychologically and linguistically motivated theories regarding the di↵erence between irony and sarcasm, these typically do not carry over to a use in predictive models; one reason might be that th...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015