How to overcome cherry-picking in research and evaluation: Text mining? [Video]

People frequently cherry-pick “evidence” to support their preconceived conclusions. In psychology, this behavior is associated with concepts like Selective Perception and Attentional Bias, which describe how individuals focus on information that aligns with their beliefs while disregarding conflicting evidence.
The good news is that modern technology offers a solution to this problem through text mining, an AI-driven approach to analyzing textual data. Also referred to as text analytics, text mining involves extracting meaningful information from documents by identifying and exploring patterns of interest. It leverages natural language processing (NLP), a subfield of artificial intelligence and computational linguistics, to understand and process human language. By utilizing machine learning algorithms, text mining automates the analysis, reducing reliance on human interpretation and minimizing subjective biases.
For more information about text mining, please consult:
Yu, C. H., Jannasch-Pennell, A., & DiGangi, S. (2018). Enhancement of student experience management in higher education by sentiment analysis and text mining. International Journal of Technology and Educational Marketing, 8, 16-33. DOI: 10.4018/IJTEM.2018010102.
Yu, C. H., Jannasch-Pennell, A., & DiGangi, S. (2011). Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. Qualitative Report, 16, 730-744. http://www.creative-wisdom.com/pub/mirror/yu_QR.pdf