Electronic Dictionaries: For both Humans and Computers
TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Improve the effectiveness of the opinion retrieval and opinion polarity classification
Proceedings of the 17th ACM conference on Information and knowledge management
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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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.