Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.
<span>The volume of unstructured texts has increased dramatically in recent years due to the internet and the digitization of information and literature. This onslaught of data will only grow, and it will come from new and unusual sources. Thus, it will be necessary to develop new and inventive approaches and tools to process and make sense of this data. Investors in the financial markets can now get information faster than ever before thanks to the expansion of communication channels, in addition to the online availability of news and reports in text format through providers like Reuters and Bloomberg. This contains a plethora of information that is often overlooked by financial market data. In order to measure the sentiment of a text, predictive and deductive methods are applied, these methods aim at extrapolating new feautures from big data. The main objective of this study is to create and test a new system capable of predicting finance and non-finance related tweets. The convolutional neural network (CNN) and latent dirichlet allocation (LDA) algorithms are used in the proposed approche. The suggested model's correctness is tested against a benchmark financial dataset, and the results demonstrate that with a database of 1,000,000 data points, our model is 99% accurate.</span>
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