User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings.
Speech is one of the major communication tools to share information among people. This exchange method has a complicated construction consisting of not the best imparting of voice but additionally consisting of the transmission of many-speaker unique information. The most important aim of this research is to extract individual features through the speech-dependent health monitoring and management system; through this system, the speech data can be collected from a remote location and can be accessed. The experimental analysis shows that the proposed model has a good efficiency. Consequently, in the last 5 years, many researchers from this domain come in front to explore various aspects of speech which includes speech analysis using mechanical signs, human system interaction, speaker, and speech identification. Speech is a biometric that combines physiological and behavioural characteristics. Especially beneficial for remote attack transactions over telecommunication networks, the medical information of each person is quite a challenge, e.g., like COVID-19 where the medical team has to identify each person in a particular region that how many people got affected by some disease and took a quick measure to get protected from such diseases and what are the safety measure required. Presently, this task is the most challenging one for researchers. Therefore, speech-based mechanisms might be useful for tracking his/her voice quality or throat getting affected. By collecting the database of people matched and comparing with his/her original database, it can be identified in such scenarios. This provides the better management system without touching and maintains a safe distance data that can be gathered and processed for further medical treatment. Many research studies have been done but speech-dependent approach is quite less and it requires more work to provide such a smart system in society, and it may be possible to reduce the chances to come into contact with viral effected people in the future and protect society for the same.
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