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Examples of secondary sources:
Mercurio, S. (2024). What About Corruption? A Text Analytics Method for a Scoping Literature Review. In: Giordano, G., Misuraca, M. (eds) New Frontiers in Textual Data Analysis. JADT 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-55917-4_28
Bashar, M. A., Nayak, R., & Balasubramaniam, T. (2020). Topic, Sentiment and Impact Analysis: COVID19 Information Seeking on Social Media. https://doi.org/10.48550/ARXIV.2008.12435
Bashar, M. A., Nayak, R., & Balasubramaniam, T. (2022). Deep learning based topic and sentiment analysis: COVID19 information seeking on social media. Social Network Analysis and Mining, 12(1), 90. https://doi.org/10.1007/s13278-022-00917-5
Bashar, M. A., Nayak, R., Knapman, G., Turnbull, P., & Fforde, C. (2023). An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains. Social Science Computer Review, 41(6), 2293–2317. https://doi.org/10.1177/08944393231158788
Bianchino, A., Fusco, D., & Pisciottano, D. (2021). How to Measure the Touristic Competitiveness: A Mixed Mode Model Proposal. ATHENS JOURNAL OF TOURISM, 8(2), 131–146. https://doi.org/10.30958/ajt.8-2-4
Das, S. D., Bala, P. K., & Das, S. (2024). Exploiting User-Generated Content in Product Launch Videos to Compute a Launch Score. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2024.3381541
Indraccolo, U., Losavio, E., & Carone, M. (2023). Applying graph theory to improve the quality of scientific evidence from textual information: Neural injuries after gynaecologic pelvic surgery for genital prolapse and urinary incontinence. Neurourology and Urodynamics, 42(3), 669–679. https://doi.org/10.1002/nau.25133
Mitra, S., & Jenamani, M. (2020). OBIM: A computational model to estimate brand image from online consumer review. Journal of Business Research, 114, 213–226. https://doi.org/10.1016/j.jbusres.2020.04.003
Ovadia, C., & Indraccolo, U. (2021). A Clinical & Experimental Obstetrics and Gynecology survey on ursodeoxycholic treatment of intrahepatic cholestasis of pregnancy: Scholars’ opinion. Clinical and Experimental Obstetrics & Gynecology, 48(6), 1300. https://doi.org/10.31083/j.ceog4806206
Santomauro, G., Alderuccio, D., Ambrosino, F., & Migliori, S. (2021). Ranking Cryptocurrencies by Brand Importance: A Social Media Analysis in ENEAGRID. In V. Bitetta, I. Bordino, A. Ferretti, F. Gullo, G. Ponti, & L. Severini (Eds.), Mining Data for Financial Applications (Vol. 12591, pp. 92–100). Springer International Publishing. https://doi.org/10.1007/978-3-030-66981-2_8
Schlaile, M. P., Bogner, K., & Muelder, L. (2021). It’s more than complicated! Using organizational memetics to capture the complexity of organizational culture. Journal of Business Research, 129, 801–812. https://doi.org/10.1016/j.jbusres.2019.09.035
Comment:Connections between words are established based on their co-occurrence within a specified proximity, such as within a sentence. Pre-processing of natural language is preliminary [sic] used to refine texts, involving tasks like eliminating stopwords and word affixes through stemming. The proximity in the illustration is not one sentence but three significant words. And it is curious that the "e" of purple is -- I infer -- an "affix" (to allow for purplish, etc?) but the "en" of golden is not. Perhaps good sources on the "Semantic Brand Score" put this more convincingly. Hoary (talk) 23:41, 30 September 2024 (UTC)[reply]
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Comment: Stemming and sources. Stemming of words like "golden" does not remove word affixes because of the Porter's stemming algorithm. It can be tested here http://text-processing.com/demo/stem/ and here https://9ol.es/porter_js_demo.html. Added additional secondary sources well describing the metric.This article is significantly different than the one previously deleted (and with much more secondary sources).WarmKomorebi (talk) 09:30, 18 October 2024 (UTC)