Branch 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
Recommended Citation
Smarandache, Florentin; Kritika Mishra; Ilanthenral Kandasamy; and Vasantha Kandasamy W.B..
"A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis."
Symmetry
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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