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The objective was to investigate the utility of cottonseed oil (CSO) as a raw material for the synthesis of CSO water-based alkyd resin. The synthesis involved the polymerization of CSO, trimethylolpropane, phthalic anhydride (PA) and trimellitic anhydride (TMA). The prepared resin coating material was subsequently applied to the surface of steel structure material.

This study aimed to synthesize water-based alkyd resins using CSO. Therefore, the alkyd resin was introduced with TMA containing carboxyl groups and neutralized with triethylamine (TEA) to form a water-soluble salt. Then, the esterification kinetics of CSO water-based alkyd resin were investigated, and finally, the basic properties of CSO water-based alkyd resin coating were evaluated.

It was demonstrated that CSO water-based alkyd resin exhibited excellent water solubility and that the esterification kinetic of the synthesis reaction could be described by a second-order reaction. The coating properties of the material were investigated and found to have good basic properties, with 40% resin addition having the best corrosion resistance. Consequently, it could be effectively applied to the surface of steel structural materials.

This study not only met the requirement of environmentally friendly development but also expanded the application of CSO through the synthesis of CSO water-based alkyd resin via alcoholysis. Compared to fatty acid process, the alcoholysis reduced the need for fatty acid pre-extraction, simplifying the alkyd resin synthesis process. Thus, economic costs are effectively reduced.

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.