Semantic analysis of amazon customer using LSTM

Under the current advancements, the whole globe is rapidly changing. With the Internet being used in every sector, it has become a fundamental requirement. The rapid development of social media applications has allowed users to voice their opinions on commonplace issues. Getting feedback from custom...

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Bibliographic Details
Main Authors: Ahlawat, Aditya, Yadav, Rajkumar
Format: Conference Proceeding
Language:English
Subjects:
Customers
Data mining
Machine learning
Sentiment analysis
Online Access:Get full text
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Summary:Under the current advancements, the whole globe is rapidly changing. With the Internet being used in every sector, it has become a fundamental requirement. The rapid development of social media applications has allowed users to voice their opinions on commonplace issues. Getting feedback from customers, users of public services, etc. is essential. Sentiment analysis, often known as opinion mining, is a common conversational pre-planning activity that aims to identify the underlying emotions associated with different types of texts. Academics in the field of sentiment analysis have recently shifted their attention to assessing public opinion on a wide range of topics, from popular media to consumer items to common societal issues. The proposed work is to investigate the results of different sentiment analysis performed on Amazon customers. The proposed study employs a hybrid method, in which Low short term memory (LSTM) based machine learning is combined with a classifier and optimizer to provide accurate results with high performance.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0199451