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This paper compares the results from quantitative text mining to qualitative expert reviews to identify emerging technologies in the fields of solar photovoltaics (PV), wind power, ocean and tidal energy, hydropower. The text mining analysis is based on the software “Tools for Innovation Monitoring” (TIM). The TIM software extracts a set of relevant keywords from a corpus of pertinent scientific publications. TIM outputs are compared to those extracted by the software VOSviewer, showing agreement. The top 300 ranked keywords are the optimum trade-off between retrieved technologies and analyst efforts. The emerging technologies identified by the experts can be retrieved in the top 300 keywords with a probability of 65 %, 25 %, depending on the technology sector and the algorithm adopted. The more salient keywords tend to correspond to technologies with an established and univocal jargon such as: "dye sensitised solar cells" or "vertical axis wind turbines". Two methods are here used and compared: the frequency of author keywords and the term frequency-inverse document frequency (TF-IDF) algorithm. The comparison of their performances is not showing a general prevalence of one method against the other, but rather a different suitability to different technology sectors.
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Stored only when both papers are in the corpus — 32 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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