A Comparative Study on Cosine Similarity Algorithm and Vector Space Model Algorithm on Document Searching

2015 ◽  
Vol 21 (10) ◽  
pp. 3321-3323
Author(s):  
Warnia Nengsih
Keyword(s):  
Comparative Study ◽  
Vector Space ◽  
Vector Space Model ◽  
Cosine Similarity ◽  
Space Model ◽  
Similarity Algorithm ◽  
Model Algorithm

scholarly journals ASPECT BASED SENTIMENT ANALYSIS DATA KUESIONER DI RUMAH SAKIT MUHAMMADIYAH LAMONGAN MENGGUNAKAN ALGORITMA K-NN.

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 506
Author(s):  
Mustain Mustain Mustain
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
Analysis Data ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Space Model

Kesulitan untuk mengorganisir data kuesioner yang bersifat konvensional melatarbelakangi penelitian ini. Oleh karena itu dibuat sistem yang memudahkan pengelompokan data kuesioner secara otomatis yang lengkap dengan sentimen yang terkandung didalamnya. Dataset yang digunakan dalam penelitian ini adalah data kuesioner rumah sakit Muhammadiyah lamongan. Penelitian ini hanya menangani kuesioner yang berbentuk teks. Data dengan fisik kertas direkap kemudian diinput ke database lengkap dengan kategori unit kerja dan sentiment. Selanjutnya dataset tersebut di dilakukan pre-prosesing yang meliputi penanganan negasi case folding, tokenizing, filtering dan stemming. Sebagai data uji komentar dari kuesioner akan dilakukan pre-prosesing selanjutnya dihitung tingkat kemiripan document dengan menggunakan metode K- Nearest Neighbor dan Vector Space Model. Jumlah data yang ditangani mempengaruhi performa system terutama dari akurasi dan kecepatan pada saat proses klasifikasi. Hasil dari sistem yang dibuat berupa ranking dokumen yang paling mirip dengan dataset berdasarkan urutan nilai cosine similarity. Ujicoba klasifikasi berdasarkan kelas kategori menghasilkan nilai akurasi 91 %. Ujicoba berdasarkan Kelas Sentimen sebesar 94 %.dari kombinasi keduanya system berhasil mendapat akurasi sebesar 86 %

scholarly journals A Comprehensive Comparative Study Using Vector Space Model with K-Nearest Neighbor on Text Categorization Data

2007 ◽  
Vol 2 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Wa`el Musa Hadi ◽  
Fadi Thabtah ◽  
Salahideen Mousa ◽  
Samer Al Hawari ◽  
Ghassan Kanaan ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vector Space ◽  
Text Categorization ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
K Nearest Neighbor ◽  
Space Model

scholarly journals Automating case definitions using literature-based reasoning

2013 ◽  
Vol 04 (04) ◽  
pp. 515-527 ◽  
Author(s):  
R. Ball ◽  
T. Botsis
Keyword(s):  
Vector Space ◽  
Semantic Network ◽  
Vector Space Model ◽  
Classification Performance ◽  
Case Definition ◽  
Cosine Similarity ◽  
Case Definitions ◽  
Space Model ◽  
Research Activities ◽  
Clinical Surveillance

SummaryBackground: Establishing a Case Definition (CDef) is a first step in many epidemiological, clinical, surveillance, and research activities. The application of CDefs still relies on manual steps and this is a major source of inefficiency in surveillance and research.Objective: Describe the need and propose an approach for automating the useful representation of CDefs for medical conditions.Methods: We translated the existing Brighton Collaboration CDef for anaphylaxis by mostly relying on the identification of synonyms for the criteria of the CDef using the NLM MetaMap tool. We also generated a CDef for the same condition using all the related PubMed abstracts, processing them with a text mining tool, and further treating the synonyms with the above strategy. The co-occur-rence of the anaphylaxis and any other medical term within the same sentence of the abstracts supported the construction of a large semantic network. The ‘islands’ algorithm reduced the network and revealed its densest region including the nodes that were used to represent the key criteria of the CDef. We evaluated the ability of the “translated” and the “generated” CDef to classify a set of 6034 H1N1 reports for anaphylaxis using two similarity approaches and comparing them with our previous semi-automated classification approach.Results: Overall classification performance across approaches to producing CDefs was similar, with the generated CDef and vector space model with cosine similarity having the highest accuracy (0.825±0.003) and the semi-automated approach and vector space model with cosine similarity having the highest recall (0.809±0.042). Precision was low for all approaches.Conclusion: The useful representation of CDefs is a complicated task but potentially offers substantial gains in efficiency to support safety and clinical surveillance.Citation: Botsis T, Ball R. Automating case definitions using literature-based reasoning. Appl Clin Inf 2013; 4: 515–527http://dx.doi.org/10.4338/ACI-2013-04-RA-0028

scholarly journals An Efficient Journal Articles Searching using Vector Space Model Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Azis Alvriyanto ◽  
Muhammad Taufiq Nuruzzaman ◽  
Maria Ulfah Siregar ◽  
Rahmat Hidayat
Keyword(s):  
Vector Space ◽  
Evaluation Criteria ◽  
Vector Space Model ◽  
Journal Articles ◽  
Space Model ◽  
Search Results ◽  
Document Frequency ◽  
Traditional Algorithm ◽  
Baseline Algorithm ◽  
Model Algorithm

One of the main feature of digital library is a search engine which depends on keywords submitted by a user. However, in the traditional algorithm, the computation performance, searching speed, significantly relies on the number of journal articles stored in the databases. Some irrelevant search results also increase the speed of article searching process. To solve the problem, in this paper we propose vector space model (VSM) algorithm to search for relevant journal articles. The VSM algorithm considers a term frequency - inversed document frequency (TF-IDF). The VSM algorithm will be compared to the baseline algorithm namely traditional algorithm. Both algorithms will be evaluated using combination of keywords which can be a synonym, phrase, error typography, or suffix and prefix. By using the data consist of 635 journal articles, both algorithms are compared in terms of 11 evaluation criteria. The results show that VSM algorithm is able to obtain the intended journal at 5th rank on average as compared to the traditional algorithm which can obtain the intended journal at rank of 171st on average. Therefore, our proposed algorithm can improve the performance to accurately sort the journal articles based on the submitted keywords as compared to traditional algorithm.   

scholarly journals PENENTUAN MULTIPLE MEMBERSHIP DOKUMEN

2017 ◽  
Vol 15 (2) ◽  
Author(s):  
Stephanie Betha R.H
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Cosine Similarity ◽  
Space Model ◽  
Multiple Membership

Multiple membership merupakan keanggotaan yang dimiliki oleh seseorang pada beberapa komunitas. Multiple membership pada dokumen artinya suatu dokumen dapat mengandung konten dari beberapa jenis kategori. Jenis kategori pada dokumen dapat ditentukan dengan mengukur kemiripan dokumen tersebut dengan kategori yang ada. Vector Space Model adalah suatu model yang digunakan untuk mengukur kemiripan antara suatu dokumen dan suatu query dengan mewakili setiap dokumen dalam sebuah koleksi sebagai sebuah titik dalam ruang vektor. Hasil dari pengukuran kemiripan tersebut merupakan nilai cosine similarity antara vektor query dari dokumen terhadap vektor kategori. Permasalahan yang terjadi adalah suatu pengukuran kemiripan vektor query dokumen, dapat menghasilkan nilai cosine similarity dengan selisih yang kecil antara vektor kategori satu dengan vektor kategori lain. Hal ini menyebabkan kedua vektor kategori tersebut menjadi saling dominan satu sama lain pada dokumen. Oleh karena itu, dibutuhkan suatu nilai batas untuk menentukan kondisi kapan suatu vektor kategori dapat dinyatakan sebagai vektor kategori yang saling dominan. Penetapan nilai batas ini menggunakan K-Means Clustering. Nilai batas ini ditetapkan berdasarkan pengelompokkan nilai jarak antar presentase cosine similarity pada suatu dokumen. Penentuan multiple membership dokumen ini akan dilakukan pada atribut judul dan kata kunci pada dokumen publikasi ilmiah.

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