3 septembre 2021
https://www.openedition.org/12554 , info:eu-repo/semantics/openAccess
Marco Vassallo et al., « Polarity Imbalance in Lexicon-based Sentiment Analysis », Accademia University Press, ID : 10.4000/books.aaccademia.8964
Polarity imbalance is an asymmetric situation that occurs while using parametric threshold values in lexicon-based Sentiment-Analysis (SA). The variation across the thresholds may have an opposite impact on the prediction of negative and positive polarity. We hypothesize that this may be due to asymmetries in the data or in the lexicon, or both. We carry out therefore experiments for evaluating the effect of lexicon and of the topics addressed in the data. Our experiments are based on a weighted version of the Italian linguistic resource MAL (Morphologically-inflected Affective Lexicon) by using as weighting corpus TWITA, a large-scale corpus of messages from Twitter in Italian. The novel Weighted-MAL (W-MAL), presented for the first time int this paper, achieved better polarity classification results especially for negative tweets, along with alleviating the aforementioned polarity imbalance.