An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales
As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are o...
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MDPI AG
2025-01-01
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| Series: | Journal of Intelligence |
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| Online Access: | https://www.mdpi.com/2079-3200/13/1/11 |
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| author | Zhen Huang Yitian Long Kaiping Peng Song Tong |
| author_facet | Zhen Huang Yitian Long Kaiping Peng Song Tong |
| author_sort | Zhen Huang |
| collection | DOAJ |
| description | As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. The ESAA utilizes OpenAI’s text-embedding-3-large model to generate high-dimensional semantic vectors (i.e., embeddings) of scale items and applies hierarchical clustering to group semantically similar items, revealing potential redundancy. Three preliminary experiments evaluated the ESAA’s ability to (1) identify semantically similar items, (2) differentiate semantically distinct items, and (3) uncover overlap between scales of concepts known for redundancy issues. Additionally, comparative analyses assessed the ESAA’s robustness and incremental validity against the advanced chatbots based on GPT-4. The results demonstrated that the ESAA consistently produced stable outcomes and outperformed all evaluated chatbots. As an objective approach for analyzing relationships between concepts operationalized as scales, the ESAA holds promise for advancing research on theory refinement and scale optimization. |
| format | Article |
| id | doaj-art-1250ade9d90f4b729ebba49cdddcbfe4 |
| institution | Kabale University |
| issn | 2079-3200 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Journal of Intelligence |
| spelling | doaj-art-1250ade9d90f4b729ebba49cdddcbfe42025-01-24T13:36:23ZengMDPI AGJournal of Intelligence2079-32002025-01-011311110.3390/jintelligence13010011An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by ScalesZhen Huang0Yitian Long1Kaiping Peng2Song Tong3School of Social Sciences, Tsinghua University, Beijing 100084, ChinaWuhan Britain-China School, Wuhan 430000, ChinaSchool of Social Sciences, Tsinghua University, Beijing 100084, ChinaSchool of Social Sciences, Tsinghua University, Beijing 100084, ChinaAs psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. The ESAA utilizes OpenAI’s text-embedding-3-large model to generate high-dimensional semantic vectors (i.e., embeddings) of scale items and applies hierarchical clustering to group semantically similar items, revealing potential redundancy. Three preliminary experiments evaluated the ESAA’s ability to (1) identify semantically similar items, (2) differentiate semantically distinct items, and (3) uncover overlap between scales of concepts known for redundancy issues. Additionally, comparative analyses assessed the ESAA’s robustness and incremental validity against the advanced chatbots based on GPT-4. The results demonstrated that the ESAA consistently produced stable outcomes and outperformed all evaluated chatbots. As an objective approach for analyzing relationships between concepts operationalized as scales, the ESAA holds promise for advancing research on theory refinement and scale optimization.https://www.mdpi.com/2079-3200/13/1/11redundancy detectionpsychological scalesGPThierarchical clustering |
| spellingShingle | Zhen Huang Yitian Long Kaiping Peng Song Tong An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales Journal of Intelligence redundancy detection psychological scales GPT hierarchical clustering |
| title | An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales |
| title_full | An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales |
| title_fullStr | An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales |
| title_full_unstemmed | An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales |
| title_short | An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales |
| title_sort | embedding based semantic analysis approach a preliminary study on redundancy detection in psychological concepts operationalized by scales |
| topic | redundancy detection psychological scales GPT hierarchical clustering |
| url | https://www.mdpi.com/2079-3200/13/1/11 |
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