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Results 321-328 of 328 (Search time: 0.003 seconds).
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Issue DateTitleAuthor(s)
2020-06OBIM: A computational model to estimate brand image from online consumer reviewMitra, Satanik
2022Sarcasm Detection in News Headlines using Supervised Learning Publisher: IEEE PDFMitra, Satanik
2022Suicidal Intention Detection in Tweets Using BERT-Based TransformersMitra, Satanik
2020-03In 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 systemsMitra, Satanik
2018SentiCon: A Concept Based Feature Set for Sentiment AnalysisMitra, Satanik
2021-05Helpfulness of online consumer reviews: A multi-perspective approachMitra, Satanik
2021-01An Approach to Identify Provocative and Problematic Content with Social NociceptorMitra, Satanik
2020-06Hybrid Improved Document-level Embedding (HIDE)Mitra, Satanik

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