ArticleHeliyon2020
Insights on next-generation manufacturing of smart devices using text analytics.
Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The MALDI Method to Analyze the Lipid Profile, Including Cholesterol, Triglycerides and Other Lipids.Current issues in molecular biology · 2026Review
- Contactless Technologies for Smart Cities: Big Data, IoT, and Cloud Infrastructures.SN computer science · 2021Article
- Recommendations for emerging air taxi network operations based on online review analysis of helicopter services.Heliyon · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the mass expansion in technological user-friendly products, there is an increasing demand for smart devices, resulting in a highly competitive novel market. To ensure sustainable success, these products must remain robust and be perceived positively by customers. With the development of Web 2.0, individuals are able to make knowledgeable purchasing decisions, specifically with the availability of millions of online customer reviews. Companies manufacturing smart devices can utilize this unstructured data to analyze the customers' perceptions of their products and identify potential improvements. To the best of our knowledge, this paper is the first to propose next-generation manufacturing insights for companies producing smart devices by determining the current strengths, weaknesses, opportunities, and threats (SWOT) of these gadgets using text analytics. A three-stage methodology is utilized, consisting of bigram and trigram examination, topic identification, and SWOT analysis. After online review extraction, comments for each smart device are separated into positive, neutral, and negative categories, based on the customer ratings. Text analytic tools are then used to determine the most frequently occurring bigrams and trigrams to provide topics for conducting the SWOT analysis. Using the SWOT technique results, numerous next-generation smart device manufacturing recommendations are presented.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.