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Issue Date	Title	Author(s)
2022	Does Cross-Functional Pedagogy of Teaching a Course Help in Management Education?: Evidence From a Supply Chain Management Course	Nagpal, Gaurav; Jasti, Naga Vamsi Krishna
2023-01	Challenges in Adoption of Business Analytics by Small Retailers: An Empirical Study in the Indian Context	Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
2021-11	Experiments on Fraud Detection use case with QML and TDA Mapper	Mitra, Satanik
2020-06	OBIM: A computational model to estimate brand image from online consumer review	Mitra, Satanik
2022	Sarcasm Detection in News Headlines using Supervised Learning Publisher: IEEE PDF	Mitra, Satanik
2022	Suicidal Intention Detection in Tweets Using BERT-Based Transformers	Mitra, Satanik
2020-03	In recent times, word embeddings are taking a significant role in sentiment analysis. As the generation of word embeddings needs huge corpora, many applications use pretrained embeddings. In spite of the success, word embeddings suffers from certain drawbacks such as it does not capture sentiment information of a word, contextual information in terms of parts of speech tags and domain-specific information. In this work we propose HIDE a Hybrid Improved Document level Embedding which incorporates domain information, parts of speech information and sentiment information into existing word embeddings such as GloVe and Word2Vec. It combine improved word embeddings into document level embeddings. Further, Latent Semantic Analysis (LSA) has been used to represent documents as a vectors. HIDE is generated, combining LSA and document level embeddings, which is computed from improved word embeddings. We test HIDE with six different datasets and shown considerable improvement over the accuracy of existing pretrained word vectors such as GloVe and Word2Vec. We further compare our work with two existing document level sentiment analysis approaches. HIDE performs better than existing systems	Mitra, Satanik
2018	SentiCon: A Concept Based Feature Set for Sentiment Analysis	Mitra, Satanik
2021-05	Helpfulness of online consumer reviews: A multi-perspective approach	Mitra, Satanik
2021-01	An Approach to Identify Provocative and Problematic Content with Social Nociceptor	Mitra, Satanik