Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198055318> ?p ?o ?g. }
- W3198055318 endingPage "121158" @default.
- W3198055318 startingPage "121158" @default.
- W3198055318 abstract "With the advent of web 2.0 websites, the impact of customers on each other's purchasing decisions has increased, and this has changed the decision-making procedure, attitude, and consumer purchase behavior. Along with increasing the volume of customers' reviews on sites, the need for a method that can be used to rank alternative products considering a set of product features and the customer comments related to these features on websites is essential. Many attempts have been made aimed at product ranking through online customer reviews (OCRs), which have their own shortcomings. These limitations occur during different stages, including focusing only on specified features on online shopping sites or extracting features based on term frequency. Other limitations are not paying attention to low repetition, but to only important features (in identifying features), not paying attention to neutral expressions including hesitations or uncertainty in consumers’ purchase decisions (in identifying the sentiment orientations of each review), using expert-based approaches to determine the weight of features (in determining the weight of features), and ranking merely based on the star rating and ignoring the valuable information in OCRs or not considering the robustness of decision-making and most effective features (in ranking alternatives). Accordingly, this study aims to narrow these gaps by proposing an integrated framework that combines sentiment analysis (SA) and multi-criteria decision-making (MCDM) techniques based on intuitionistic fuzzy sets (IFS). In the following, the developed hybrid model has been used in a real-world case to rank five mobile phone products using the OCRs on the Amazon site to illustrate the availability and utility of the proposed methodology. Sensitivity analysis has been performed to determine the most robust method and most effective features." @default.
- W3198055318 created "2021-09-13" @default.
- W3198055318 creator A5014750772 @default.
- W3198055318 creator A5018476984 @default.
- W3198055318 creator A5059934493 @default.
- W3198055318 creator A5060689953 @default.
- W3198055318 date "2021-12-01" @default.
- W3198055318 modified "2023-10-13" @default.
- W3198055318 title "An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making" @default.
- W3198055318 cites W1969240017 @default.
- W3198055318 cites W1971875039 @default.
- W3198055318 cites W1975641760 @default.
- W3198055318 cites W1980564456 @default.
- W3198055318 cites W1981107087 @default.
- W3198055318 cites W1987177581 @default.
- W3198055318 cites W1998676999 @default.
- W3198055318 cites W2014639527 @default.
- W3198055318 cites W2016389981 @default.
- W3198055318 cites W2018804614 @default.
- W3198055318 cites W2019443512 @default.
- W3198055318 cites W2022829918 @default.
- W3198055318 cites W2026244788 @default.
- W3198055318 cites W2027342001 @default.
- W3198055318 cites W2030439497 @default.
- W3198055318 cites W2036962258 @default.
- W3198055318 cites W2049274974 @default.
- W3198055318 cites W2053379819 @default.
- W3198055318 cites W2060166243 @default.
- W3198055318 cites W2067505258 @default.
- W3198055318 cites W2068692334 @default.
- W3198055318 cites W2070824330 @default.
- W3198055318 cites W2071497929 @default.
- W3198055318 cites W2081037298 @default.
- W3198055318 cites W2086389500 @default.
- W3198055318 cites W2088121285 @default.
- W3198055318 cites W2106703595 @default.
- W3198055318 cites W2106800165 @default.
- W3198055318 cites W2123490190 @default.
- W3198055318 cites W2139886241 @default.
- W3198055318 cites W2141631351 @default.
- W3198055318 cites W2160660844 @default.
- W3198055318 cites W2169064565 @default.
- W3198055318 cites W2171468534 @default.
- W3198055318 cites W2231191608 @default.
- W3198055318 cites W2282429601 @default.
- W3198055318 cites W2289253436 @default.
- W3198055318 cites W2345493290 @default.
- W3198055318 cites W2475388955 @default.
- W3198055318 cites W2550799801 @default.
- W3198055318 cites W2564996866 @default.
- W3198055318 cites W2588990050 @default.
- W3198055318 cites W2735447265 @default.
- W3198055318 cites W2741252866 @default.
- W3198055318 cites W2751040599 @default.
- W3198055318 cites W2769743636 @default.
- W3198055318 cites W2811148006 @default.
- W3198055318 cites W2883101305 @default.
- W3198055318 cites W2885574217 @default.
- W3198055318 cites W2911576302 @default.
- W3198055318 cites W2911591129 @default.
- W3198055318 cites W2912083585 @default.
- W3198055318 cites W2912711975 @default.
- W3198055318 cites W2939352329 @default.
- W3198055318 cites W2939943368 @default.
- W3198055318 cites W2942471816 @default.
- W3198055318 cites W2956096227 @default.
- W3198055318 cites W2963253499 @default.
- W3198055318 cites W2963788533 @default.
- W3198055318 cites W2969124341 @default.
- W3198055318 cites W2979571941 @default.
- W3198055318 cites W2982638854 @default.
- W3198055318 cites W3005765040 @default.
- W3198055318 cites W3008063828 @default.
- W3198055318 cites W3008218502 @default.
- W3198055318 cites W3009876158 @default.
- W3198055318 cites W3033532169 @default.
- W3198055318 cites W3035557392 @default.
- W3198055318 cites W3045913839 @default.
- W3198055318 cites W3092340601 @default.
- W3198055318 cites W3093035895 @default.
- W3198055318 cites W3097702310 @default.
- W3198055318 cites W3123399111 @default.
- W3198055318 cites W3124946654 @default.
- W3198055318 cites W3175258587 @default.
- W3198055318 cites W3177343170 @default.
- W3198055318 cites W3194388628 @default.
- W3198055318 cites W4205785508 @default.
- W3198055318 cites W4211007335 @default.
- W3198055318 cites W609503659 @default.
- W3198055318 doi "https://doi.org/10.1016/j.techfore.2021.121158" @default.
- W3198055318 hasPublicationYear "2021" @default.
- W3198055318 type Work @default.
- W3198055318 sameAs 3198055318 @default.
- W3198055318 citedByCount "19" @default.
- W3198055318 countsByYear W31980553182021 @default.
- W3198055318 countsByYear W31980553182022 @default.
- W3198055318 countsByYear W31980553182023 @default.
- W3198055318 crossrefType "journal-article" @default.