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UPAR7: A knowledge-based system for headline sentiment tagging
For the Affective Text task at SemEval- 2007, University Paris 7’s system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and …...
Generating High-Coverage Semantic Orientation Lexicons From Overtly Marked Words and a Thesaurus
Sentiment analysis often relies on a se- mantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lex- icons, but they tend …...
Generating Shifting Sentiment for a Conversational Agent
We investigate techniques for generating al- ternative output sentences with varying sen- timent, using (an approximation to) the Valentino method, based on SentiWordNet, of Guerini et al. We extend this …...
Interpreting SentiWordNet for Opinion Classification
AbstractWe describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, …...
Q-WordNet: Extracting Polarity from WordNet Senses
AbstractThis paper presents Q-WordNet, a lexical resource consisting of WordNet senses automatically annotated by positive and negative polarity. Polarity classification amounts to decide whether a text (sense, sentence, etc.) may …...
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
AbstractIn this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a …...
An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style
AbstractInteractive story systems often involve dialogue with virtual dramatic characters. However, to date most character dialogue is written by hand. One way to ease the authoring process is to (semi-)automatically …...
FBK: Sentiment Analysis in Twitter with Tweetsted
This paper presents the Tweetsted system im- plemented for the SemEval 2013 task on Sen- timent Analysis in Twitter. In particular, we participated in Task B on Message Polar- ity …...
Sentiment Lexicon Interpolation and Polarity Estimation of Objective and Out-Of-Vocabulary Words to Improve Sentiment Classification on Microblogging
Sentiment analysis has become an important classification task because a large amount of user-generated content is published over the Internet. Sentiment lexicons have been used successfully to classify the sentiment …...
DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter
The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figura- tive Language in Twitter. The proposed ap- proach employs syntactical and morphological features, which indicate sentiment polarity …...
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization
Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary. These con- cepts are often obtained by selecting …...
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction
In this paper, we present our contribution in SemEval2016 task71: Determining Senti- ment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised …...
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification
This paper describes our approach to the Se- mEval 2016 task 4, “Sentiment Analysis in Twitter”, where we participated in subtask A. Our system relies on AlchemyAPI and Senti- WordNet …...
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
AbstractThis paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can …...
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings
AbstractThis paper presents a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages: Danish, Norwegian and Swedish. This method benefits from the English Sentiwordnet and …...