Keyword extraction from social media via AHP

Ramay, Waheed Yousuf; Cheng-Yin, Xu; Illahi, Inam

doi:10.3233/hsm-180344

Cited by 2 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Multi Attribute Decision Making (MADM) methods used Analytical Hierarchical Process to rank keywords Ramay et al. ( 2018 ) and keyphrases Garg and Kumar ( 2018b ) in the document using different graphical properties. The ranking of nodes in GoW is evaluated using the performance evaluation measures for ranking of keywords, namely, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR).…”

Section: Ranking Of Nodes In Graph Of Wordsmentioning

confidence: 99%

“…A keyword Ramay et al. ( 2018 ) and keyphrase ranking Garg and Kumar ( 2018a ) techniques are proposed by using the Multiple Attributes Decision Making (MADM) approach, namely, Analytical Hierarchical Process (AHP).…”

Section: Ranking Of Nodes In Graph Of Wordsmentioning

confidence: 99%

“…In recent times, as academic researchers started working on the structure of WCN of different textual documents Liang et al (2009), Gao et al (2014, the graphical features are used for generating an objective function to provide an optimized solution by ranking nodes. A keyword Ramay et al (2018) and keyphrase ranking Garg and Kumar (2018a) techniques are proposed by using the Multiple Attributes Decision Making (MADM) approach, namely, Analytical Hierarchical Process (AHP).…”

Section: Madm Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A survey on different dimensions for graphical keyword extraction techniques

Garg

2021

Artif Intell Rev

View full text Add to dashboard Cite

Section: Ranking Of Nodes In Graph Of Wordsmentioning

confidence: 99%

Section: Ranking Of Nodes In Graph Of Wordsmentioning

confidence: 99%

Section: Madm Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A survey on different dimensions for graphical keyword extraction techniques

Garg

2021

Artif Intell Rev

View full text Add to dashboard Cite

“…Therefore, this paper will take small and medium-sized enterprises as the research object, and select the main indicators reflecting the low carbon operation results of enterprises. Build a low carbon operation evaluation index system for enterprises (Ramay et al, 2019). Then, based on the Analytic Hierarchy Process (AHP) model, each indicator of the enterprise's low-carbon operation performance is processed to obtain the weight of each evaluation element.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Xie

2023

Front. Energy Res.

View full text Add to dashboard Cite

With the arrival of the low carbon era, enterprises, as the main body of the market economy, must take the road of low carbon operation whether to fulfill their social responsibilities or to seek enterprise development. However, Chinese enterprises started late to understand the low-carbon economy. So far, except for a few state-owned enterprises, some leading private enterprises and foreign-funded enterprises, they have made remarkable achievements in low-carbon emission reduction. Most enterprises in China have not realized the importance of low carbon emission reduction. In order to step out of the “high carbon” era and coordinate economic development and environmental protection, China must solve the problem of low-carbon transformation of small and medium-sized manufacturing enterprises. How to measure the degree of low carbon transformation, and how the government objectively evaluates the degree of low carbon economic development of enterprises, with a view to formulating corresponding standard incentives and punishment measures for them. It is urgent to establish a sound, reasonable and feasible low carbon operation management indicator system, and comprehensively evaluate the information related to low carbon operation of SMEs through reasonable selection of indicators. According to the connotation of low-carbon enterprises, the economic benefits of enterprises are closely combined with environmental benefits. A set of low carbon operation performance evaluation index system including economy, technology and environment has been constructed. The AHP index weight is calculated by establishing the AHP model and the Data Envelopment Analysis (DEA) index weight is calculated by establishing the DEA model. On this basis, the grey relational evaluation model based on AHP-DEA is established. Then the paper evaluates the performance of representative enterprises’ low carbon operation. Propose corresponding improvement suggestions for the problems reflected in the low carbon operation performance ranking of enterprises and the scoring of various indicators. The research results show that the algorithm has high efficiency and the accuracy of model evaluation is 95.51%. To a certain extent, this study makes up for the research results of the impact of digital transformation on corporate strategic performance, and also provides ideas for the research of corporate financial performance evaluation.

show abstract

Keyword extraction from social media via AHP

Cited by 2 publications

References 9 publications

A survey on different dimensions for graphical keyword extraction techniques

A survey on different dimensions for graphical keyword extraction techniques

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Contact Info

Product

Resources

About