Sentiment analysis using KNIME: a systematic literature review of big data logistics

Text analytics and sentiment analysis can help researchers to derive potentially valuable thematic and narrative insights from text-based content such as industry reviews, leading OM and OR journal articles and government reports. The classification system described here analyses the opinions of the performance of various public and private, manufacturing, medical, service and retail organizations in integrating big data into their logistics. It explains methods of data collection and the sentiment analysis process for classifying big data logistics literature using KNIME. Finally, it then gives an overview of the differences and explores future possibilities in sentiment analysis for investigating different industrial sectors and data sources.