부산대학교 도서관

Numerous research have applied sentiment analysis to Twitter because of the vast amounts of data being created there. These social media platforms produce huge amounts of unstructured data streams that are difficult to deal with. Massive volumes of unstructured data are produced by such social network services, making their management extremely difficult. The study's goal is to use a consensus reached among a variety of algorithms to consistently evaluate the sentiment of trending tweets in the Twitter API data stream. We used a variety of algorithms, including Support-Vector Machine and Naive Bayes, to arrive at a consensus on the tone of trending tweets in the Twitter API data stream. We used a number of different approaches, such as Support-Vector Machine and Naive Bayes. Combining the best features of TextBlob and the Lexicon Method. We hypothesize that by combining these techniques, we will be able to obtain more reliable findings. Lexicon Method and Textblob Storage. We hypothesize that by combining these approaches, we will be able to provide more reliable findings.