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The purpose of this paper is to evaluate the existing scenarios for 3D printing (3DP) in order to identify the “white space” where future opportunities have not been proposed or developed to date. Based around aspects of order penetration points, geographical scope and type of manufacturing, these gaps are identified.

A structured literature review has been carried out on both academic and trade publications. As of the end of May 2016, this identified 128 relevant articles containing 201 future scenarios. Coding these against aspects of existing manufacturing and supply chain theory has led to the development of a framework to identify “white space” in the existing thinking.

The coding shows that existing future scenarios are particularly concentrated on job shop applications and pull-based supply chain processes, although there are fewer constraints on geographical scope. Five distinct areas of “white space” are proposed, reflecting various opportunities for future 3DP supply chain development.

Being a structured literature review, there are potentially articles not identified through the search criteria used. The nature of the findings is also dependent upon the coding criteria selected. However, these are theoretically derived and reflect important aspect of strategic supply chain management.

Practitioners may wish to explore the development of business models within the “white space” areas.

Currently, existing future 3DP scenarios are scattered over a wide, multi-disciplinary literature base. By providing a consolidated view of these scenarios, it is possible to identify gaps in current thinking. These gaps are multi-disciplinary in nature and represent opportunities for both academics and practitioners to exploit.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.