Sentimental analysis on voice using AWS comprehend

Title

Sentimental analysis on voice using AWS comprehend

Creator

Satyanarayana G.; Bhuvana J.; Balamurugan M.

Description

Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file. 2020 IEEE.

Source

2020 International Conference on Computer Communication and Informatics, ICCCI 2020

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Aws Athena; Aws Comprehend; Aws Glue; Lambda; NLP; Quick sight; S3; Sentimental analysis; Transcribe

Coverage

Satyanarayana G., Jain University, Department of MCA (SCT), Bangalore, India; Bhuvana J., Jain University, Department of MCA, Bangalore, India; Balamurugan M., Christ University, Department of CSE, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-172814513-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCCI48352.2020.9104105

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