A Sentiment Polarity Analyser based on a Lexical-Probabilistic Approach

In this paper, we propose an unsupervised approach to automatically classify the sentiment polarity of texts that can be documents or tweets related to the user’s favorite hashtags. The system is based on a combination of probabilistic and lexicon-based approaches. We first apply the Latent Dirichlet Allocation (LDA) model to discover two vectors of terms relevant for two topics (presumably positive and negative) and then we calculate the polarity of the associated sentiment using the SentiWordnet resource. Experiments have been conducted first on an English dataset and then the system has been associated to an application and tested for Italian. Results show that the system can partition the polarity with a good accuracy.

• Type: Journal
• Year: 2015
• Where: AIIA
• Authors: Berardina De Carolis, Domenico Redavid, Angelo Bruno
• Keywords: AIIA 2015, Artificial Intelligence