Maple, Mathematica, and Matlab: the 3M's without the tape

Chonacky, Norman; Winch, David M.

doi:10.1109/mcse.2005.18

Cited by 12 publications

(4 citation statements)

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Section: Methodology Of Researchmentioning

confidence: 99%

“…If compared to other software (i.e. R or MatLab), Wolfram Mathematica offers a wider amount of different functions to run several analyses in a huge range of knowledge including biological and social science (Chonacky and Winch, 2005). The study collected the tweets related to three fast fashion accessories retailers based in London (UK), posted in February 2018, comprising a total amount of 9,652 tweets (2,552 for retailer A; 4,900 per retailer B; 2,200 for retailer C respectively).…”

Section: Twitter Samplingmentioning

confidence: 99%

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Making sense of consumers’ tweets

Pantano

Giglio

Dennis

2019

IJRDM

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Purpose Consumers online interactions, posts, rating and ranking, reviews of products/attractions/restaurants and so on lead to a massive amount of data that marketers might access to improve the decision-making process, by impacting the competitive and marketing intelligence. The purpose of this paper is to develop understanding of consumers online-generated contents in terms of positive or negative comments to increase marketing intelligence. Design/methodology/approach The research focuses on the collection of 9,652 tweets referring to three fast fashion retailers of different sizes operating in the UK market, which have been shared among consumers and between consumer and firm, and subsequently evaluated through a sentiment analysis based on machine learning. Findings Findings provide the comparison and contrast of consumers’ response toward the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence. Practical implications The research provides an effective and systemic approach to accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online and investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence. Originality/value To best of the authors’ knowledge, while other authors tried to identify the effect of positive or negative online comments/posts/reviews, the present study is the first one to show how to systematically detect the positive or negative sentiments of shared tweets for improving the marketing intelligence of fast fashion retailers.

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Section: Methodology Of Researchmentioning

confidence: 99%

Section: Twitter Samplingmentioning

confidence: 99%

Making sense of consumers’ tweets

Pantano

Giglio

Dennis

2019

IJRDM

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“…By contrast, three commercial software packages for high-productivity scientific and engineering computation-Maple, Mathematica, and MATLAB-had by then become very popular. 3 These systems continue to be widely used in education, industry, and government settings. Their purchase price and proprietary implementations, however, led many champions of open-source software to conceive alternatives, oftentimes closely imitating their functionality.…”

mentioning

confidence: 99%

The Python/Jupyter Ecosystem: Today’s Problem-Solving Environment for Computational Science

Barba

2021

Comput. Sci. Eng.

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“…It is also worth noting that a desktop version of MATLAB, used by many engineering professors, was introduced in 2000 [8]. Other computing packages, such as Mathcad, Aspen Plus, Maple, Matematica, and Microsoft Excel also grew in popularity around that time [9]- [11], [24], [27].…”

Section: Time Period For Citations Included In This Literature Reviewmentioning

confidence: 99%