revision 1, almost done

literature/Thesis.bib

@@ -11,6 +11,49 @@
   abstract = {Rule 4 is thereplication rule. The replication rule is a natural follow-up to rule 3, ``Build reality checks into your research.'' Rule 3 advises you to look for ways to cross-check your results both internally---using other information in your data set---and externally---using different methods and data sets. In multiple-method research, as described in the previous chapter, your aim is to see if different methods and different sorts of data lead to the same conclusions.Rule 4 advises replication---the identical analysis (same measures, models, and estimation methods) of parallel data sets (different samples of the same}
 }
 
+@article{sang-woonResearchPaperClassification2019,
+  title = {Research Paper Classification Systems Based on {{TF-IDF}} and {{LDA}} Schemes},
+  author = {Kim, Sang-Woon and Gil, Joon-Min},
+  year = 2019,
+  month = aug,
+  journal = {Human-centric Computing and Information Sciences},
+  volume = {9},
+  number = {1},
+  pages = {30},
+  issn = {2192-1962},
+  doi = {10.1186/s13673-019-0192-7},
+  urldate = {2024-12-16},
+  abstract = {With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.},
+  langid = {english},
+  keywords = {Artificial Intelligence,K-means clustering,LDA,Paper classification,TF-IDF},
+  file = {/home/michaelb/Zotero/storage/23YFBPYR/Kim and Gil - 2019 - Research paper classification systems based on TF-IDF and LDA schemes.pdf}
+}
+
+@inproceedings{ramosUsingTFIDFDetermine2003,
+  title = {Using {{TF-IDF}} to {{Determine Word Relevance}} in {{Document Queries}}},
+  author = {Ramos, J. E.},
+  year = 2003,
+  urldate = {2026-05-18},
+  abstract = {In this paper, we examine the results of applying Term Frequency Inverse Document Frequency (TF-IDF) to determine what words in a corpus of documents might be more favorable to use in a query. As the term implies, TF-IDF calculates values for each word in a document through an inverse proportion of the frequency of the word in a particular document to the percentage of documents the word appears in. Words with high TF-IDF numbers imply a strong relationship with the document they appear in, suggesting that if that word were to appear in a query, the document could be of interest to the user. We provide evidence that this simple algorithm efficiently categorizes relevant words that can enhance query retrieval.}
+}
+
+@article{gonzalez-salaCaracterizacionPsicologiaJuridica2017,
+  title = {Characterization of {{Legal Psychology}} through Psychology Journals Included in {{Criminology}} \& {{Penology}} and {{Law}} Categories of {{Web}} of {{Science}}},
+  author = {{Gonz{\'a}lez-Sala}, Francisco and {Osca-Lluch}, Julia and Tortosa Gil, Francisco and Pe{\~n}aranda Ortega, Mar{\'i}a},
+  year = 2017,
+  month = mar,
+  journal = {Anales de Psicolog\'ia},
+  volume = {33},
+  number = {2},
+  pages = {411},
+  issn = {1695-2294, 0212-9728},
+  doi = {10.6018/analesps.33.2.262591},
+  urldate = {2026-05-18},
+  abstract = {The objective of this work is to learn about the most relevant aspects that characterize contemporary Legal Psychology throughout the study of journals included in the WoS between the years 2009 and 2014 related with the area of Psychology. The number of selected publications is 16, mainly from the USA and Great Britain. The results show an increase in the number of works and authors, a greater collaboration and a growth in medium productors. It exists a major presence of men in editorial boards and as authors, outstanding the figures of T. Ward in 2009 and A. Vrij in 2014. According to the analysis of key words the most relevant themes during these years have been Crime, Conduct, Woman and Meta-analysis, being sexual violence towards children and women and gender violence the criminal typology most studied.},
+  copyright = {http://revistas.um.es/analesps/about/submissions\#copyrightNotice},
+  file = {/home/michaelb/Zotero/storage/KC3L68AL/González-Sala et al. - 2017 - Characterization of Legal Psychology through psychology journals included in Criminology & Penology.pdf}
+}
+
 @inproceedings{abdennourEnsembleLearningModel2023,
   title = {Ensemble {{Learning Model}} for~{{Medical Text Classification}}},
   booktitle = {Web {{Information Systems Engineering}} -- {{WISE}} 2023},