Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894577651> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2894577651 abstract "Sentiment analysis is a trendy domain of Machine Learning which has developed considerably in the last several years. It helps to determine the sentiment of a user in an utterance, a document or a review. Some systems can extract the target of the sentiment, in order to distinguish separated sentiments over the different aspects of the product. Recommender systems (RS) are also more and more used in everyday life due to the rise of the Big Data era. We can count 3 types of Recommender Systems: the ones using Collaborative Filtering, the ones which are Content Based and the hybrid ones which are melting several kinds of information in various proportions. The general recommender systems are using global features, modeling the interest of the user on a specific topic, but they use neither the sentimental information nor the interest and preferences of the user over the different aspects that can be found in the recommended items. The opinions contained in the reviews can help to disentangle the user's preferences over the different aspects, modeling the user more confidently as well as the general opinion of the crowd over the product. By analyzing the reviews available in the Web, Sentiment Analysis systems can help improving the recommender systems wether they are simple, aspect-based or end-to-end deep models. This paper outlines a short review of the recommender systems enhanced by sentiment analysis modules." @default.
- W2894577651 created "2018-10-12" @default.
- W2894577651 creator A5027708777 @default.
- W2894577651 creator A5056398585 @default.
- W2894577651 date "2018-01-01" @default.
- W2894577651 modified "2023-10-11" @default.
- W2894577651 title "Short Review of Sentiment-Based Recommender Systems" @default.
- W2894577651 cites W1880280183 @default.
- W2894577651 cites W1981856681 @default.
- W2894577651 cites W1987068480 @default.
- W2894577651 cites W2003670608 @default.
- W2894577651 cites W2015886158 @default.
- W2894577651 cites W2047676437 @default.
- W2894577651 cites W2116959421 @default.
- W2894577651 cites W2131305515 @default.
- W2894577651 cites W2134353060 @default.
- W2894577651 cites W2142972908 @default.
- W2894577651 cites W2152184085 @default.
- W2894577651 cites W2165112010 @default.
- W2894577651 cites W2513620429 @default.
- W2894577651 cites W2742657630 @default.
- W2894577651 cites W2748186218 @default.
- W2894577651 cites W2749348810 @default.
- W2894577651 cites W2798331900 @default.
- W2894577651 cites W4211173887 @default.
- W2894577651 cites W4300175872 @default.
- W2894577651 cites W2256041226 @default.
- W2894577651 doi "https://doi.org/10.1145/3240117.3240120" @default.
- W2894577651 hasPublicationYear "2018" @default.
- W2894577651 type Work @default.
- W2894577651 sameAs 2894577651 @default.
- W2894577651 citedByCount "4" @default.
- W2894577651 countsByYear W28945776512020 @default.
- W2894577651 countsByYear W28945776512021 @default.
- W2894577651 countsByYear W28945776512022 @default.
- W2894577651 countsByYear W28945776512023 @default.
- W2894577651 crossrefType "proceedings-article" @default.
- W2894577651 hasAuthorship W2894577651A5027708777 @default.
- W2894577651 hasAuthorship W2894577651A5056398585 @default.
- W2894577651 hasConcept C10138342 @default.
- W2894577651 hasConcept C134306372 @default.
- W2894577651 hasConcept C136764020 @default.
- W2894577651 hasConcept C154945302 @default.
- W2894577651 hasConcept C162324750 @default.
- W2894577651 hasConcept C182306322 @default.
- W2894577651 hasConcept C21569690 @default.
- W2894577651 hasConcept C23123220 @default.
- W2894577651 hasConcept C2524010 @default.
- W2894577651 hasConcept C33923547 @default.
- W2894577651 hasConcept C36503486 @default.
- W2894577651 hasConcept C41008148 @default.
- W2894577651 hasConcept C557471498 @default.
- W2894577651 hasConcept C66402592 @default.
- W2894577651 hasConcept C90673727 @default.
- W2894577651 hasConceptScore W2894577651C10138342 @default.
- W2894577651 hasConceptScore W2894577651C134306372 @default.
- W2894577651 hasConceptScore W2894577651C136764020 @default.
- W2894577651 hasConceptScore W2894577651C154945302 @default.
- W2894577651 hasConceptScore W2894577651C162324750 @default.
- W2894577651 hasConceptScore W2894577651C182306322 @default.
- W2894577651 hasConceptScore W2894577651C21569690 @default.
- W2894577651 hasConceptScore W2894577651C23123220 @default.
- W2894577651 hasConceptScore W2894577651C2524010 @default.
- W2894577651 hasConceptScore W2894577651C33923547 @default.
- W2894577651 hasConceptScore W2894577651C36503486 @default.
- W2894577651 hasConceptScore W2894577651C41008148 @default.
- W2894577651 hasConceptScore W2894577651C557471498 @default.
- W2894577651 hasConceptScore W2894577651C66402592 @default.
- W2894577651 hasConceptScore W2894577651C90673727 @default.
- W2894577651 hasLocation W28945776511 @default.
- W2894577651 hasLocation W28945776512 @default.
- W2894577651 hasOpenAccess W2894577651 @default.
- W2894577651 hasPrimaryLocation W28945776511 @default.
- W2894577651 hasRelatedWork W1484355083 @default.
- W2894577651 hasRelatedWork W2041004656 @default.
- W2894577651 hasRelatedWork W2098758514 @default.
- W2894577651 hasRelatedWork W2103058005 @default.
- W2894577651 hasRelatedWork W2119611366 @default.
- W2894577651 hasRelatedWork W2170391450 @default.
- W2894577651 hasRelatedWork W2202724490 @default.
- W2894577651 hasRelatedWork W2735929803 @default.
- W2894577651 hasRelatedWork W3008845055 @default.
- W2894577651 hasRelatedWork W4220714703 @default.
- W2894577651 isParatext "false" @default.
- W2894577651 isRetracted "false" @default.
- W2894577651 magId "2894577651" @default.
- W2894577651 workType "article" @default.