Sentiment Classification Based on Phonetic Characteristics

Autor:	Sergei Ermakov, Liana Ermakova
Rok vydání:	2013
Předmět:	Russian language Training set business.industry Computer science Speech recognition Sentiment analysis Artificial intelligence Affect (linguistics) computer.software_genre business computer Natural language processing Word (computer architecture)
Zdroj:	Lecture Notes in Computer Science ISBN: 9783642369728 ECIR
DOI:	10.1007/978-3-642-36973-5_65
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2298b0cb71faed2ea6d7ebd37d99d229 https://doi.org/10.1007/978-3-642-36973-5_65 Zobrazit plný text záznamu