Matches in SemOpenAlex for { <https://semopenalex.org/work/W4286593267> ?p ?o ?g. }
- W4286593267 endingPage "1922" @default.
- W4286593267 startingPage "1905" @default.
- W4286593267 abstract "Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach." @default.
- W4286593267 created "2022-07-22" @default.
- W4286593267 creator A5032996381 @default.
- W4286593267 creator A5062200853 @default.
- W4286593267 creator A5076470975 @default.
- W4286593267 date "2022-06-10" @default.
- W4286593267 modified "2023-09-28" @default.
- W4286593267 title "Sentiment Analysis of Twitter Data" @default.
- W4286593267 cites W1482898105 @default.
- W4286593267 cites W1839863673 @default.
- W4286593267 cites W1985834397 @default.
- W4286593267 cites W2020060453 @default.
- W4286593267 cites W2031998113 @default.
- W4286593267 cites W2044547964 @default.
- W4286593267 cites W2089597365 @default.
- W4286593267 cites W2095579012 @default.
- W4286593267 cites W2112935688 @default.
- W4286593267 cites W2143394965 @default.
- W4286593267 cites W2143455647 @default.
- W4286593267 cites W2143570397 @default.
- W4286593267 cites W2144012961 @default.
- W4286593267 cites W2149167588 @default.
- W4286593267 cites W2169816422 @default.
- W4286593267 cites W2218244741 @default.
- W4286593267 cites W2333697551 @default.
- W4286593267 cites W2344287567 @default.
- W4286593267 cites W2464619766 @default.
- W4286593267 cites W2472594302 @default.
- W4286593267 cites W2507305386 @default.
- W4286593267 cites W2590061102 @default.
- W4286593267 cites W2596012080 @default.
- W4286593267 cites W2598121424 @default.
- W4286593267 cites W2601018171 @default.
- W4286593267 cites W2612769033 @default.
- W4286593267 cites W2755320143 @default.
- W4286593267 cites W2766108548 @default.
- W4286593267 cites W3105931033 @default.
- W4286593267 cites W3121587504 @default.
- W4286593267 cites W4205184193 @default.
- W4286593267 cites W4240156601 @default.
- W4286593267 cites W4298378499 @default.
- W4286593267 cites W627358517 @default.
- W4286593267 doi "https://doi.org/10.4018/978-1-6684-6303-1.ch101" @default.
- W4286593267 hasPublicationYear "2022" @default.
- W4286593267 type Work @default.
- W4286593267 citedByCount "0" @default.
- W4286593267 crossrefType "book-chapter" @default.
- W4286593267 hasAuthorship W4286593267A5032996381 @default.
- W4286593267 hasAuthorship W4286593267A5062200853 @default.
- W4286593267 hasAuthorship W4286593267A5076470975 @default.
- W4286593267 hasConcept C119857082 @default.
- W4286593267 hasConcept C121158502 @default.
- W4286593267 hasConcept C12267149 @default.
- W4286593267 hasConcept C124101348 @default.
- W4286593267 hasConcept C136764020 @default.
- W4286593267 hasConcept C154945302 @default.
- W4286593267 hasConcept C15744967 @default.
- W4286593267 hasConcept C161191863 @default.
- W4286593267 hasConcept C169258074 @default.
- W4286593267 hasConcept C2522767166 @default.
- W4286593267 hasConcept C2780586970 @default.
- W4286593267 hasConcept C41008148 @default.
- W4286593267 hasConcept C518677369 @default.
- W4286593267 hasConcept C52001869 @default.
- W4286593267 hasConcept C66402592 @default.
- W4286593267 hasConcept C75684735 @default.
- W4286593267 hasConcept C77805123 @default.
- W4286593267 hasConcept C95623464 @default.
- W4286593267 hasConceptScore W4286593267C119857082 @default.
- W4286593267 hasConceptScore W4286593267C121158502 @default.
- W4286593267 hasConceptScore W4286593267C12267149 @default.
- W4286593267 hasConceptScore W4286593267C124101348 @default.
- W4286593267 hasConceptScore W4286593267C136764020 @default.
- W4286593267 hasConceptScore W4286593267C154945302 @default.
- W4286593267 hasConceptScore W4286593267C15744967 @default.
- W4286593267 hasConceptScore W4286593267C161191863 @default.
- W4286593267 hasConceptScore W4286593267C169258074 @default.
- W4286593267 hasConceptScore W4286593267C2522767166 @default.
- W4286593267 hasConceptScore W4286593267C2780586970 @default.
- W4286593267 hasConceptScore W4286593267C41008148 @default.
- W4286593267 hasConceptScore W4286593267C518677369 @default.
- W4286593267 hasConceptScore W4286593267C52001869 @default.
- W4286593267 hasConceptScore W4286593267C66402592 @default.
- W4286593267 hasConceptScore W4286593267C75684735 @default.
- W4286593267 hasConceptScore W4286593267C77805123 @default.
- W4286593267 hasConceptScore W4286593267C95623464 @default.
- W4286593267 hasLocation W42865932671 @default.
- W4286593267 hasOpenAccess W4286593267 @default.
- W4286593267 hasPrimaryLocation W42865932671 @default.
- W4286593267 hasRelatedWork W2749258744 @default.
- W4286593267 hasRelatedWork W3149206686 @default.
- W4286593267 hasRelatedWork W3204641204 @default.
- W4286593267 hasRelatedWork W4226485841 @default.
- W4286593267 hasRelatedWork W4280611221 @default.
- W4286593267 hasRelatedWork W4283016678 @default.
- W4286593267 hasRelatedWork W4288767723 @default.
- W4286593267 hasRelatedWork W4312733094 @default.
- W4286593267 hasRelatedWork W4327531511 @default.