Sentiment classification based on phonetic characteristics

Abstract

The majority of sentiment classifiers is based on dictionaries or requires large amount of training data. Unfortunately, dictionaries contain only limited data and machine-learning classifiers using word-based features do not consider part of words, which makes them domain-specific, less effective and not robust to orthographic mistakes. We attempt to overcome these drawbacks by developing a context-independent approach. Our main idea is to determine some phonetic features of words that could affect their sentiment polarity. These features are applicable to all words; it eliminates the need to continuous manual dictionary renewal. Our experiments are based on a sentiment dictionary for the Russian language. We apply phonetic features to predict word sentiment based on machine learning. © 2013 Springer-Verlag.