Exploring Sentiment Analysis Algorithms in Power Generation for Automated Monitoring

this paper explores diverse sentiment evaluation algorithms used inside the automated monitoring of strength technology. Unique algorithms such as Naive Bayes, guide Vector Machines, Conditional Random Fields, and RNNs are mentioned and compared with recognition to their performance in this use case. The results imply the fine-acting fashions for every algorithm, with F1 rankings ranging from seventy-one. Three% (Naive Bayes) to 83.1 % (RNNs), permitting the discovery of diffused insight into the strength era area. Moreover, an evaluation of the fake positives generated via each algorithm will assist in refining the performance of the algorithms in the power technology domain.