Tandem LSTM SVM Approach for Sentiment Analysis
English. In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection. Italiano. In questo articolo descriviamo il sistema che abbiamo utilizzato per affrontare i diversi compiti del task SENTIPOLC della conferenza EVALITA 2016. In questa edizione abbiamo partecipato a tutti i sotto compiti nella configurazione vincolata, cioe senza utilizzare risorse annotate a mano diverse rispetto a quelle distribuite dagli organizzatori. Per questa partecipazione abbiamo sviluppato un metodo che combina una rete neurale ricorrente di tipo Long Short Term Memory, utilizzate per apprendere lo spazio delle feature e per catturare dipendenze temporali, e Support Vector Machine per la classificazione. Le SVM combinano la rappresentazione del documento prodotta da LSTM con un ampio insieme di features che descrivono la struttura lessicale e grammaticale del testo. Attraverso questo sistema abbiamo ottenuto la seconda posizione nella classificazione della Soggettivita, la terza posizione nella classificazione della Polarita e la sesta nella identificazione dell'Ironia. 1 Description of the system We addressed the EVALITA 2016 SENTIPOLC task (Barbieri et al. 2016) as a three classification problem: two binary classification tasks (Subjectivity Classification and Irony Detection) and a four class classification task (Polarity Classification) We implemented a tandem LSTM SVM classifier operating on morpho syntactically tagged texts. We used this architecture since similar systems were successfully employed to tackle different classification problems such keyword spotting (Wollmer et al. 2009) or the automatic estimation of human affect from speech signal (Wollmer et al. 2010) showing that tandem architectures outperform the performances of the single classifiers. In this work we used Keras (Chollet, 2016) deep learning framework and LIBSVM (Chang et al. 2001) to generate respectively the LSTM and the SVMs statistical models. Since our approach relies on morphosyntactically tagged texts, both training and test data were automatically morphosyntactically tagged by the POS tagger described in (Dell'Orletta, 2009) In addition, in order to improve the overall accuracy of our system (described in 1.2) we developed sentiment polarity and word embedding lexicons described below. All the created lexicons are made freely available at the following website: http:/www.italianlp.it/
CLiC it/EVALITA