A New Novel Approach for Sentiments Analysis using Contextual Mining and Supervised Learning
Sachin Vyawhare, Shashi Bhushan, Sapna R. Tayde, Mrunali Jaiswal · Computology Journal of Applied Computer Science and Intelligent Technologies · 2024
Textual data mining is used to anticipate the sentiment of a user based on a similar book. Using conditional probability distributions, the rating similarity between books can be quantified by textual mining. In supervised learning, the input and output data are sent to the machine learning model in tandem to maximize accuracy. In this paper, a cloud-based suggested system is incorporated which detects and recommends similar types of content based on the ranking of books. A cloud recommendation system is a means of determining which service is best suited to the user’s tastes and requirements. The work uses specific formats such as graphics or text, and the networks built throughout the process may help uncover the links between the words. Textual mining is used to calculate book-ranging similarities and recommendations. From the comparison result, the proposed logistic regression technique has maximum accuracy which is 82% as compared to the existing technique The model shows all recommended books that are like the input book are displayed, while all other types of books are hidden from view. Also, positive, or negative masking can be determined by comparing the intensity of the extracted images.