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Issue Date	Title	Author(s)
2020-02	Redesigning the efficiency process analysis for working capital models: Evidences from the determinants	Sharma, Satyendra Kumar; Chadha, Saurabh
2020-09	Assessing working capital management efficiency of Indian manufacturing exporters	Chadha, Saurabh; Sharma, Satyendra Kumar
2020-03	Trends in Organizational Behavior: A Systematic Review and Research Directions	Mahesh, Jayashree
2020-11	Evolving face of workplace learning and development: a case of an Indian HR consulting firm	Naim, Mohammad Faraz
2020-06	Linking entrepreneurship, innovation and economic growth: evidence from GEM countries	Chadha, Saurabh; Dutta, Nirankush
2020-10	Exploring Deal of the Day: an e-commerce strategy	Nigam, Achint
2020	A Review of Innovation Diffusion Modelling Literature	Nagpal, Gaurav; Chanda, Udayan
2020-06	OBIM: A computational model to estimate brand image from online consumer review	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
2020-06	Hybrid Improved Document-level Embedding (HIDE)	Mitra, Satanik