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We introduce a novel machine learning frame- work based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment …...

We propose a semi-supervised bootstrap- ping algorithm for analyzing China’s for- eign relations from the People’s Daily. Our approach addresses sentiment tar- get clustering, subjective lexicons extrac- tion and sentiment …...

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analy- sis in Twitter. This was the most popular sen- timent analysis shared task to …...

This paper reports our submission to the Aspect-Based Sentiment Analysis task of Se- mEval 2016. It covers the prediction of sen- timent for a given set of aspects (e.g., sub- …...

AbstractWe describe a supervised system that uses optimized Condition Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of …...

AbstractVector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each …...

AbstractIn this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models …...

AbstractThe growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on …...