Combining Overall and Target Oriented Sentiment Analysis over Portuguese Text from Social Media

• Main Author: Saias, José
• Publication Date: 2015
• Other Authors: Silva, Ruben, Oliveira, Eduardo, Ruiz, Ruben
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10174/14879; https://doi.org/10.14738/tmlai.33.1297
• topic: Sentiment Analysis; NLP; Opinion Mining; Machine Learning; Text classification

This document describes an approach to perform sentiment analysis on social media Portuguese content. In a single system, we perform polarity classification for both the overall sentiment, and target oriented sentiment. In both modes we train a Maximum Entropy classifier. The overall model is based on BoW type features, and also features derived from POS tagging and from sentiment lexicons. Target oriented analysis begins with named entity recognition, followed by the classification of sentiment polarity on these entities. This classifier model uses features dedicated to the entity mention textual zone, including negation detection, and the syntactic function of the target occurrence segment. Our experiments have achieved an accuracy of 75% for target oriented polarity classification, and 97% in overall polarity.