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Connectedness and Stratification of Single-Valued Neutrosophic Topological Spaces
Open AccessArticle

A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

1
Shell India Markets, RMZ Ecoworld Campus, Marathahalli, Bengaluru, Karnataka 560103, India
2
School of Computer Science and Engineering, VIT, Vellore 632014, India
3
Department of Mathematics, University of New Mexico, 705 Gurley Avenue, Gallup, NM 87301, USA
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(10), 1715; https://doi.org/10.3390/sym12101715
Received: 6 September 2020 / Revised: 28 September 2020 / Accepted: 9 October 2020 / Published: 18 October 2020
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. View Full-Text
Keywords: sentiment analysis; speech analysis; neutrosophic sets; indeterminacy; Single-Valued Neutrosophic Sets (SVNS); clustering algorithm; K-means; hierarchical agglomerative clustering sentiment analysis; speech analysis; neutrosophic sets; indeterminacy; Single-Valued Neutrosophic Sets (SVNS); clustering algorithm; K-means; hierarchical agglomerative clustering
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Mishra, K.; Kandasamy, I.; Kandasamy W. B., V.; Smarandache, F. A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis. Symmetry 2020, 12, 1715.

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