Author
Duşçu, Mihail, Danış, Dilek Günneç
Publication Date
2020-11
Publication Place
-
Elsevier
Subject
Audio processing, Machine learning, Sentiment analysis, Text normalization, Twitter
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0306-4573
Record ID
7c8a3245-5bd1-44c6-9e23-8cd424d31932
Library Location
Industrial Engineering
Date
2020-11
Sample Text
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
