Polarity classification of twitter messages using audio processing

Name: Polarity classification of twitter messages using audio processing
Author: Duşçu, Mihail, Danış, Dilek Günneç

İsim	Polarity classification of twitter messages using audio processing
Yazar	Duşçu, Mihail, Danış, Dilek Günneç
Basım Tarihi:	2020-11
Basım Yeri	- Elsevier
Konu	Audio processing, Machine learning, Sentiment analysis, Text normalization, Twitter
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0306-4573
Kayıt Numarası	7c8a3245-5bd1-44c6-9e23-8cd424d31932
Lokasyon	Industrial Engineering
Tarih	2020-11
Örnek Metin	Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.
DOI	10.1016/j.ipm.2020.102346
Cilt	57

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Polarity classification of twitter messages using audio processing

Yazar Duşçu, Mihail, Danış, Dilek Günneç

Basım Tarihi 2020-11

Basım Yeri - Elsevier

Konu Audio processing, Machine learning, Sentiment analysis, Text normalization, Twitter

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0306-4573

Kayıt Numarası 7c8a3245-5bd1-44c6-9e23-8cd424d31932

Lokasyon Industrial Engineering

Tarih 2020-11

Örnek Metin Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.

DOI 10.1016/j.ipm.2020.102346

Cilt 57