Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal on Recent and Innovation Trends in Computing and Communication
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.