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.
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Ermakov, S., Ermakova, L. (2013). Sentiment Classification Based on Phonetic Characteristics. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_65
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DOI: https://doi.org/10.1007/978-3-642-36973-5_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
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