An experimental approach to compare various deep learning architectures for sentiment analysis

This paper aims to study the efficiency of various seq2seq deep learning architectures for the solution of toxic speech classification and performing efficient sentiment analysis using unilingual publicly available dataset. Numerical examples are presented along with various validation metrics and g...

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Bibliographic Details
Main Authors: Ghorpade, Parag, Lende, Akash, Chavan, Laukik, Shaikh, Rehan, Shaikh, Nuzhat, Chavan, Ajinkya
Format: Conference Proceeding
Language:English
Subjects:
Bidirectional encoder representations from transformers
Deep learning
Natural language processing
Recurrent neural networks
Sentiment analysis
Seq2Seq
Online Access:Request full text
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Summary:This paper aims to study the efficiency of various seq2seq deep learning architectures for the solution of toxic speech classification and performing efficient sentiment analysis using unilingual publicly available dataset. Numerical examples are presented along with various validation metrics and graphs to indicate the efficiency of the various NLP techniques and confirm the experimental findings of the paper. We also compare and contrast between traditionally used natural language processing models and state of the art model like Bidirectional Encoder Representations from Transformers or BERT.
ISSN:2642-7354
DOI:10.1109/ICCCA49541.2020.9250785