Mukul Aggarwal

Work place: KIET Group of Institutions, Uttar Pradesh, Delhi NCR, Ghaziabad, India



Research Interests: Artificial Intelligence, Data Mining, Information Retrieval


Mukul Aggarwal is an Assistant Professor in the Department of Information Technology at KIET Group of institutions, Delhi-NCR, Ghaziabad. Mr. Aggarwal is an academician, researcher, having 15 years of teaching experience. Mr. Aggarwal has been pro-actively involved with professional associations and associated with various professional bodies: Life Membership of “Computer Society of India” (CSI), Life Membership of “The Indian Science Congress Association” (ISCA), Membership of “The International Association of Engineers” (IAENG), Membership of “International Association of Computer Science and Information Technology” (IACSIT). He has participated as chaired/reviewed in several numbers of Conferences, Workshops and Seminars. His research area includes Information Retrieval, Semantic Web, Artificial Intelligence, and Data Mining. He has attended and coordinated many FDP / seminars/workshops / Conferences.

Author Articles
An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model

By Shashank Mishra Mukul Aggarwal Shivam Yadav Yashika Sharma

DOI:, Pub. Date: 8 Oct. 2023

A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.

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