Mathematics and Statistics Faculty and Staff Publications

Document Type

Article

Publication Date

10-2020

Abstract

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

Publisher

MDPI

Publication Title

Symmetry

Volume

12

Issue

1715

First Page

1

Last Page

22

DOI

doi:10.3390/sym12101715

Language (ISO)

English

Keywords

sentiment analysis; Speech Analysis; Neutrosophic Sets; indeterminacy; Single-Valued Neutrosophic Sets (SVNS); clustering algorithm; K-means; hierarchical agglomerative clustering

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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