Using Text Mining to Classify Research Papers

Sulova, Snezhana

doi:10.5593/sgem2017/21/s07.083

Cited by 19 publications

(8 citation statements)

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“…18 One of the problems in text classification is the number of attributes or dimensions so that many irrelevant attributes in the data set cause the classifier's performance to not run optimally. 19 For this reason, it is necessary to have a technique to increase effectiveness and reduce dimensions that are too large through the selection of features or terms, 20 such as within-document TF, weighting with one of the popular methods, namely TF-IDF (which sees how important a word is in a collection of corpus), 21 and binary representation which looks at the absence and presence of a concept in a document 22 by converting it to 0 and 1. 23…”

Section: Knn Classifiermentioning

confidence: 99%

Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy

Santosa

2023

ITAL

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The National Nuclear Energy Agency of Indonesia (BATAN) taxonomy is a nuclear competence field organized into six categories. The Polytechnic Institute of Nuclear Technology, as an institution of nuclear education, faces a challenge in organizing student publications according to the fields in the BATAN taxonomy, especially in the library. The goal of this research is to determine the most efficient automatic document classification model using text mining to categorize student final project documents in Indonesian and monitor the development of the nuclear field in each category. The kNN algorithm is used to classify documents and identify the best model by comparing Cosine Similarity, Correlation Similarity, and Dice Similarity, along with vector creation binary term occurrence and TF-IDF. A total of 99 documents labeled as reference data were obtained from the BATAN repository, and 536 unlabeled final project documents were prepared for prediction. In this study, several text mining approaches such as stem, stop words filter, n-grams, and filter by length were utilized. The number of k is 4, with Cosine-binary being the best model with an accuracy value of 97 percent, and kNN works optimally when working with binary term occurrence in Indonesian language documents when compared to TF-IDF. Engineering of Nuclear Devices and Facilities is the most popular field among students, while Management is the least preferred. However, Isotopes and Radiation are the most prominent fields in Nuclear Technochemistry. Text mining can assist librarians in grouping documents based on specific criteria. There is also the possibility of observing the evolution of each existing category based on the increase of documents and the application of similar methods in various circumstances. Because of the curriculum and courses given, the growth of each discipline of nuclear science in the study program is different and varied.

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Section: Knn Classifiermentioning

confidence: 99%

Exploring Final Project Trends Utilizing Nuclear Knowledge Taxonomy

Santosa

2023

ITAL

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“…The text mining process has been discussed as patterns and features based algorithm in extracting related documents. Using text mining to classify research papers [2], uses natural language processing tools towards the classification of research papers. The method has been adapted support vector machine and naïve bayes algorithms in classification.…”

Section: Procedures For Paper Submissionmentioning

confidence: 99%

Text Mining with Topical and Informative Measures using Semantic Ontology

Shobarani¹,

Arulanandham²

2019

IJITEE

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The text mining has been identified as dominant throughout the era in many scientific problems. Number of techniques have been identified and proposed earlier, each uses different features towards mining text information. However, they suffer to achieve higher performance in mining relevant text information or documents. To support the development of text mining tasks, an efficient topical and informative measures based algorithm is presented, which uses semantic ontology and the taxonomy as dictionary. The text features of the documents has been extracted to generate set of terms. For any document, the text features are used to remove the noisy features like stop words, stemming and tagging. With the noise removed pure terms, the method estimates the Topical Depth Similarity (TDS) and informative Depth Similarity (IDM) measures. The measures has been estimated towards each document to perform text mining. The input query has been estimated for the TDS measure to identify the category of the query. According to the category of query, the method estimates the TDS and IDS measures to mining the text. The proposed method improve the performance of text mining with reduces false classification ratio.

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“…Plain text documents are a vital and rapidly growing part of online information in various domains, e.g., i n biomedical, research papers, and recruitment [22,43,35]. A single plain text can contain as much information as a small, structured database.…”

Section: Introductionmentioning

confidence: 99%