An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model
Автор: Shashank Mishra, Mukul Aggarwal, Shivam Yadav, Yashika Sharma
Журнал: International Journal of Engineering and Manufacturing @ijem
Статья в выпуске: 5 vol.13, 2023 года.
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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.
Tokenizers, LSTM Model, Sentiment, NLP, Machine Learning, Binary Text Classification
Короткий адрес: https://sciup.org/15018709
IDR: 15018709 | DOI: 10.5815/ijem.2023.05.02
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