Our Faculty

Year : 2025

Legal Contract Analysis and Risk Assessment Using Pre-Trained Legal-T5 and Law-GPT

Cite this Research Publication : Poornima A, K.V Nagaraja, Manju Venugopalan, Legal Contract Analysis and Risk Assessment Using Pre-Trained Legal-T5 and Law-GPT, 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), IEEE, 2025, https://doi.org/10.1109/icicacs65178.2025.10968817

Publisher : IEEE

Year : 2024

Semantic Similarity Analysis for Resume Filtering using PySpark

Cite this Research Publication : Akhilesh P, Amal Krishna K, S Karthick Bharadwaj, Manju Venugopalan, Semantic Similarity Analysis for Resume Filtering using PySpark, 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), IEEE, 2024, https://doi.org/10.1109/i2ct61223.2024.10543433

Publisher : IEEE

Year : 2024

Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture

Cite this Research Publication : Peddi Gowtham Balaji, Priyanka Prakash Katariya, S Sruthi, Manju Venugopalan, Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture, 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), IEEE, 2024, https://doi.org/10.1109/ickecs61492.2024.10616957

Publisher : IEEE

Year : 2023

Fake News Detection Using Deep Learning and Transformer-Based Model

Cite this Research Publication : Paliwal Mohan Subhash, Deepa Gupta, Suja Palaniswamy, Manju Venugopalan, Fake News Detection Using Deep Learning and Transformer-Based Model, 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, 2023, https://doi.org/10.1109/icccnt56998.2023.10308352

Publisher : IEEE

Year : 2016

Context Specific Lexicon for Hindi Reviews

Cite this Research Publication : D. Mishra, Manju Venugopalan, and Dr. Deepa Gupta, “Context Specific Lexicon for Hindi Reviews”, in Procedia Computer Science, 2016, vol. 93, pp. 554 - 563.

Publisher : Procedia Computer Science.

Year : 2015

Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features

Cite this Research Publication : Manju Venugopalan and Dr. Deepa Gupta, “Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features”, in Mining Intelligence and Knowledge Exploration, 2015, pp. 664–670.

Publisher : Mining Intelligence and Knowledge Exploration, Springer International Publishing

Year : 2015

Exploring sentiment analysis on Twitter data

Cite this Research Publication : Manju Venugopalan and Dr. Deepa Gupta, “Exploring sentiment analysis on Twitter data”, in Eighth International Conference on Contemporary Computing (IC3), 2015.

Publisher : Eighth International Conference on Contemporary Computing (IC3)

Year : 2022

An Automated System for Identifying an Optimal Set for Text Labelling

Cite this Research Publication : Deepa Gupta, Manju Venugopalan, Peeta Basa Pati, “An Automated System for Identifying an Optimal Set for Text Labelling”, 202141044434, 28th Sep 2022.

Year : 2022

A reinforced active learning approach for optimal sampling in aspect term extraction for sentiment analysis

Cite this Research Publication : Manju Venugopalan, Gupta Deepa, A reinforced active learning approach for optimal sampling in aspect term extraction for sentiment analysis, Expert Systems with Applications,Volume 209,2022,118228,(IF: 8.665)

Publisher : Expert Systems with Applications

Year : 2022

An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis

Cite this Research Publication : Manju Venugopalan and Gupta Deepa, an enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis, Knowledge-Based Systems (2022)

Publisher : Knowledge-Based Systems

Year : 2021

A Rule Based Approach For Aspect Extraction In Hindi Reviews

Cite this Research Publication : Ojha, Chinmayee; Manju Venugopalan, Gupta, Deepa, (2021), “A Rule Based Approach For Aspect Extraction In Hindi Reviews”, Journal of intelligent and fuzzy-systems, 1-9, 2021, (IF: 1.851)

Publisher : Journal of intelligent and fuzzy-systems

Year : 2015

An Enhanced Polarity Lexicon by Learning-based Method Using Related Domain Knowledge

Cite this Research Publication : Manju Venugopalan and Dr. Deepa Gupta, “An Enhanced Polarity Lexicon by Learning-based Method Using Related Domain Knowledge”, International Journal of Information Processing and Management, vol. 6, no. 2, pp. 61–72, 2015.

Publisher : International Journal of Information Processing and Management

Year : 2021

A supervised approach to aspect term extraction using minimal robust features for sentiment analysis

Cite this Research Publication : Manju Venugopalan, Deepa Gupta , Vartika Bhatia , A supervised approach to aspect term extraction using minimal robust features for sentiment analysis. In Progress in Advanced Computing and Intelligent Engineering (pp. 237-251). Springer, Singapore.

Publisher : Springer

Year : 2020

A Statistical-Semantic PSO Model for Customer Reviews-Based Question Answering Systems

Cite this Research Publication : Dwivedi, G., Venugopalan, M., & Gupta, D. (2019). A statistical-semantic PSO model for customer reviews-based question answering systems. In International Conference on Soft Computing and Signal Processing (pp. 137-151). Springer, Singapore

Publisher : Springer

Year : 2020

Enhancing personalized response to product queries using product reviews incorporating semantic information

Cite this Research Publication : Aich, P., Venugopalan, M., & Gupta, D. (2020). Enhancing personalized response to product queries using product reviews incorporating semantic information. In Advances in Data and Information Sciences (pp. 497-509). Springer, Singapore.

Publisher : Springer

Year : 2020

An unsupervised hierarchical rule based model for aspect term extraction augmented with pruning strategies

Cite this Research Publication : Venugopalan, M., & Gupta, D. (2020). An unsupervised hierarchical rule based model for aspect term extraction augmented with pruning strategiesE. Procedia Computer Science, 171, 22-31.

Publisher : Elsevier

Year : 2019

A supervised approach for extractive text summarization using minimal robust features

Cite this Research Publication : Krishnan, D., Bharathy, P., & Venugopalan, M. (2019). A supervised approach for extractive text summarization using minimal robust features. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 521-527). IEEE.

Publisher : IEEE

Year : 2018

Content based spam detection in short text messages with emphasis on dealing with imbalanced datasets

Cite this Research Publication : Aich, P., Venugopalan, M., & Gupta, D. (2018). Content based spam detection in short text messages with emphasis on dealing with imbalanced datasets. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-5). IEEE.

Publisher : IEEE

Year : 2018

Rating Prediction Model for Reviews Using a Novel Weighted Textual Feature Method

Cite this Research Publication : Manju Venugopalan, G. Nalayini, G. Radhakrishnan & Deepa Gupta . (2018). Rating prediction model for reviews using a novel weighted textual feature method. In Recent findings in intelligent computing techniques (pp. 177-190). Springer, Singapore.

Publisher : Springer

Year : 2018

Hadoop and Natural Language Processing Based Analysis on Kisan Call Center (KCC) Data

Cite this Research Publication : V. Kasi Viswanath, Madhuri, C. Gayathri V., Raviteja, C., S. Saravanan, and Venugopalan, M., “Hadoop and Natural Language Processing Based Analysis on Kisan Call Center (KCC) Data”, International Conference on Advances in Computing, Communications and Informatics (ICACCI). PES, Bengaluru, 2018.

Publisher : International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Year : 2017

Family tree generation from electoral data to learn geographical distribution patterns

Cite this Research Publication : Bhavi, M. M., Venugopalan, M., Gupta, D., Aggarwal, A., & Mishra, C. (2017). Family tree generation from electoral data to learn geographical distribution patterns. In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (pp. 178-183). IEEE.

Publisher : IEEE