1. The average length of most abstracts is around 1,700 characters, whereas titles are around 125. The titles may contain important keywords or identifiers for a given article, similar to the abstracts.
2. NLP engine was developed to search the papers with higher similarity from Springer based on user input.
3. The most common terms were analyzed from the top seven papers with higher similarity, considering an equal number of topics.
The exponential increase in scientific literature presents both an opportunity and a challenge for researchers. While the vast volume of information can potentially fuel innovation, the current search methods make finding relevant and credible research cumbersome and time-consuming. Traditional keyword-based search engines often yield a wide array of results, many of which are irrelevant or lack credibility, making it difficult for researchers to find the needle of valuable knowledge in the haystack of information.
There is an urgent need for an advanced search mechanism that can effectively parse the ever-growing body of scientific literature, understanding nuances of terminology, and accurately identifying the relevance and credibility of articles. Addressing this issue will drastically improve researchers’ efficiency, enabling them to devote more time to actual research rather than laborious literature searching.
Text mining can revolutionize scientific literature search in several ways. By analyzing the context of words and phrases, it can interpret the nuances of scientific terminology and filter out irrelevant content, thereby improving the relevance and accuracy of search results. It can also evaluate the credibility of articles based on factors such as the reputation of the journal or the number of citations, ensuring that researchers have access to the most reliable information. Additionally, text mining can identify patterns and trends in the literature, highlighting hot topics or emerging fields of study that might otherwise be overlooked.
In essence, the application of text mining techniques in scientific literature search can significantly enhance the speed and efficiency of information retrieval, reducing the time researchers spend sifting through unrelated or low-quality articles. This way, researchers can focus more on their research work, driving innovation and progress in their respective fields.
The data of scientific papers were obtained from PubMed and Springer.
