Sunil Saumya

Orcid: 0000-0002-9555-9434

According to our database1, Sunil Saumya authored at least 27 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2023
Review helpfulness prediction on e-commerce websites: A comprehensive survey.
Eng. Appl. Artif. Intell., November, 2023

Analysis of community question-answering issues via machine learning and deep learning: State-of-the-art review.
CAAI Trans. Intell. Technol., March, 2023

Enabling Quantum Natural Language Processing for Hindi Language.
CoRR, 2023

2022
Fighting hate speech from bilingual hinglish speaker's perspective, a transformer- and translation-based approach.
Soc. Netw. Anal. Min., 2022

Spam review detection using LSTM autoencoder: an unsupervised approach.
Electron. Commer. Res., 2022

Hope Speech Detection on Social Media Platforms.
CoRR, 2022

IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages.
CoRR, 2022

SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments.
Proceedings of the Second Workshop on Language Technology for Equality, 2022

CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques.
Proceedings of the Second Workshop on Language Technology for Equality, 2022

Sentiment and Homophobia Detection on YouTube using Ensemble Machine Learning Techniques.
Proceedings of the Working Notes of FIRE 2022, 2022

2021
IIIT_DWD@LT-EDI-EACL2021: Hope Speech Detection in YouTube multilingual comments.
Proceedings of the First Workshop on Language Technology for Equality, 2021

Sentiment Analysis of Dravidian-CodeMix Language.
Proceedings of the Working Notes of FIRE 2021, 2021

An Ensemble-Based Model for Sentiment Analysis of Dravidian Code-Mixed Social Media Posts.
Proceedings of the Working Notes of FIRE 2021, 2021

Abusive and Threatening Language Detection from Urdu Social Media Posts: A machine learning approach.
Proceedings of the Working Notes of FIRE 2021, 2021

An Ensemble Approach for Hate and Offensive Language Identification in English and Indo-Aryan Languages.
Proceedings of the Working Notes of FIRE 2021, 2021

mBERT Based Model for Identification of Offensive Content in South Indian Languages.
Proceedings of the Working Notes of FIRE 2021, 2021

Hate or Non-hate: Translation based hate speech identification in Code-Mixed Hinglish data set.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Predicting the helpfulness score of online reviews using convolutional neural network.
Soft Comput., 2020

NSIT & IIITDWD @ HASOC 2020: Deep learning model for hate-speech identification in Indo-European languages.
Proceedings of the Working Notes of FIRE 2020, 2020

IIIT_DWD@HASOC 2020: Identifying offensive content in Indo-European languages.
Proceedings of the Working Notes of FIRE 2020, 2020

NITP-AI-NLP@Dravidian-CodeMix-FIRE2020: A Hybrid CNN and Bi-LSTM Network for Sentiment Analysis of Dravidian Code-Mixed Social Media Posts.
Proceedings of the Working Notes of FIRE 2020, 2020

NITP-AI-NLP@UrduFake-FIRE2020: Multi-layer Dense Neural Network for Fake News Detection in Urdu News Articles.
Proceedings of the Working Notes of FIRE 2020, 2020

NITP-AI-NLP@HASOC-Dravidian-CodeMix-FIRE2020: A Machine Learning Approach to Identify Offensive Languages from Dravidian Code-Mixed Text.
Proceedings of the Working Notes of FIRE 2020, 2020

NITP-AI-NLP@HASOC-FIRE2020: Fine Tuned BERT for the Hate Speech and Offensive Content Identification from Social Media.
Proceedings of the Working Notes of FIRE 2020, 2020

2018
Ranking online consumer reviews.
Electron. Commer. Res. Appl., 2018

2017
Sequential Purchase Recommendation System for E-Commerce Sites.
Proceedings of the Computer Information Systems and Industrial Management, 2017

2016
Predicting Stock Movements using Social Network.
Proceedings of the Social Media: The Good, the Bad, and the Ugly, 2016


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