1 research outputs found
Combining Overall and Target Oriented Sentiment Analysis over Portuguese Text from Social Media
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