Integrating Text Mining and Network Analysis for Topic Detection from Published Articles on Banana Sensory Characteristics

Abstract

Published articles (28) from PubMed database on banana sensory characteristics were from 2002 to 2018. They were mined to detect the topic of discussion using the KNIME software. The texts were tagged with the Open Source Chemistry Analysis Routines (OSCAR) chemical named entity and preprocessed by filtering and stemming, thereafter the topic of discussion detected with the Latent Dirichlet Allocation and term co-occurrence was determined using KNIME data mining software. The co-occurrence terms were converted to node adjacency matrix and imported into Gephi Graph Visualisation and Manipulation software version 0.02. Network statistics such as modularity class, degree centrality, betweenness and closeness centrality were estimated. Majority of the OSCAR tagged words (50.8%) were chemical compounds and 47.3% ontology terms. The directed network consisted of 53 nodes and 904 edges. There were four modularity classes. The terms with high betweenness centrality (>45) were, accept, fruit, analysis, coat, food, composite and banana. Three topics were detected from the documents, namely (1) quality of banana fruit and peel; (2) use of banana fruit in food and wine and (3) sensory acceptability of banana peel and flour in food products. This chapter provides details each topic.

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“…Banana is an abundant fruit crop that yields good agricultural revenue 8 . Bananas help develop other foods and buy products, i.e., flavor, stain, nutrients, fertilizers, fibers, etc.…”