FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4254256218> ?p ?o ?g. }

• W4254256218 endingPage "2261" @default.
• W4254256218 startingPage "2254" @default.
• W4254256218 abstract "Sentiments are the emotions which are communicated among individuals. These are opinions given by people on any item, product or service availed or experience online. This paper discusses that part of research area which involves the analysis of sentiments exchanged by people online that further tells how sentiments and features through online tourist reviews are extracted using deep learning techniques. Tourist behavior can be judged by tourists reviews for various tourist places, hotels and other services provided by tourism industry. The proposed idea of the paper is to show the high efficiency of deep learning techniques like CNN, RNN,LSTM to extract the features online by use of extra hidden layers. Further, comparison of these techniques as well as comparison of these techniques with machine learning classical algorithms like SVM, Naïve Bayes, KNN,RF etc has been done to show that deep learning methods are more efficient than classical machine learning algorithms. The accurate capturing of attitudes of tourists towards tourist places, hotels & other services of tourism industry plays utmost important role to enhance the business model of tourism industry. This can be done through sentiment analysis using deep learning methods efficiently. Classification of polarity will be done by extracting textual features using CNN,RNN,LSTM deep learning algorithms. Extracting features are fed to deep learning classifier to classify the review into either positive, negative or neutral type of reviews. After comparing various deep learning and classical techniques of machine learning, it has been concluded that LSTM,RNN give best results to classify reviews into positive and negative reviews rather than SVM,KNN classical techniques. In this way sentiment analysis has been done and the proposed idea of this research paper is change in the machine learning techniques or methods from classical algorithms to neural network deep learning methods which in future definitely will give better results to analyze deeply the sentiments of tourists to find out the liking and disliking of various tourist places, hotels and related tourism services that will help tourism business industry to work on the gap in existing services provided by them and system can become more efficient in future. Such improved tourism system will give benefits to tourists or users in terms of better services and undoubtedly it will help tourism industry to enhance business in future." @default.
• W4254256218 created "2022-05-12" @default.
• W4254256218 creator A5045744227 @default.
• W4254256218 creator A5058142867 @default.
• W4254256218 date "2020-05-30" @default.
• W4254256218 modified "2023-09-30" @default.
• W4254256218 title "Feature Extraction through Sentiment Analysis of Tourist Sentiments using Deep Learning Techniques like CNN, RNN and LSTM" @default.
• W4254256218 doi "https://doi.org/10.35940/ijrte.a2846.059120" @default.
• W4254256218 hasPublicationYear "2020" @default.
• W4254256218 type Work @default.
• W4254256218 citedByCount "1" @default.
• W4254256218 countsByYear W42542562182021 @default.
• W4254256218 crossrefType "journal-article" @default.
• W4254256218 hasAuthorship W4254256218A5045744227 @default.
• W4254256218 hasAuthorship W4254256218A5058142867 @default.
• W4254256218 hasBestOaLocation W42542562181 @default.
• W4254256218 hasConcept C108583219 @default.
• W4254256218 hasConcept C119857082 @default.
• W4254256218 hasConcept C12267149 @default.
• W4254256218 hasConcept C147168706 @default.
• W4254256218 hasConcept C154945302 @default.
• W4254256218 hasConcept C166957645 @default.
• W4254256218 hasConcept C18918823 @default.
• W4254256218 hasConcept C205649164 @default.
• W4254256218 hasConcept C41008148 @default.
• W4254256218 hasConcept C50644808 @default.
• W4254256218 hasConcept C52001869 @default.
• W4254256218 hasConcept C52622490 @default.
• W4254256218 hasConcept C66402592 @default.
• W4254256218 hasConcept C95623464 @default.
• W4254256218 hasConceptScore W4254256218C108583219 @default.
• W4254256218 hasConceptScore W4254256218C119857082 @default.
• W4254256218 hasConceptScore W4254256218C12267149 @default.
• W4254256218 hasConceptScore W4254256218C147168706 @default.
• W4254256218 hasConceptScore W4254256218C154945302 @default.
• W4254256218 hasConceptScore W4254256218C166957645 @default.
• W4254256218 hasConceptScore W4254256218C18918823 @default.
• W4254256218 hasConceptScore W4254256218C205649164 @default.
• W4254256218 hasConceptScore W4254256218C41008148 @default.
• W4254256218 hasConceptScore W4254256218C50644808 @default.
• W4254256218 hasConceptScore W4254256218C52001869 @default.
• W4254256218 hasConceptScore W4254256218C52622490 @default.
• W4254256218 hasConceptScore W4254256218C66402592 @default.
• W4254256218 hasConceptScore W4254256218C95623464 @default.
• W4254256218 hasIssue "1" @default.
• W4254256218 hasLocation W42542562181 @default.
• W4254256218 hasOpenAccess W4254256218 @default.
• W4254256218 hasPrimaryLocation W42542562181 @default.
• W4254256218 hasRelatedWork W2595988085 @default.
• W4254256218 hasRelatedWork W2741836081 @default.
• W4254256218 hasRelatedWork W2979979539 @default.
• W4254256218 hasRelatedWork W3136979370 @default.
• W4254256218 hasRelatedWork W3192794374 @default.
• W4254256218 hasRelatedWork W3211546796 @default.
• W4254256218 hasRelatedWork W3213901898 @default.
• W4254256218 hasRelatedWork W4205958290 @default.
• W4254256218 hasRelatedWork W4254256218 @default.
• W4254256218 hasRelatedWork W4311106074 @default.
• W4254256218 hasVolume "9" @default.
• W4254256218 isParatext "false" @default.
• W4254256218 isRetracted "false" @default.
• W4254256218 workType "article" @default.