Inverse Document Frequency in K-Nearest Neighbour (K-NN) for Competition Recommendation based on Activity in Online Learning

2021 ◽  
Author(s):  
Irawan Dwi Wahyono ◽  
Khoirudin Asfani ◽  
Mohd Murtadha Mohamad ◽  
Djoko Saryono ◽  
Hari Putranto ◽  
...  
Keyword(s):  
Online Learning ◽  
Nearest Neighbour ◽  
Inverse Document Frequency ◽  
Document Frequency

scholarly journals Klasifikasi Artikel Ilmiah Dengan Berbagai Skenario Preprocessing

2020 ◽  
Vol 2 (2) ◽  
pp. 70
Author(s):  
Hidayatul Ma'rifah ◽  
Aji Prasetya Wibawa ◽  
Muhammad Iqbal Akbar
Keyword(s):  
Text Mining ◽  
Vector Space ◽  
Cross Validation ◽  
Confusion Matrix ◽  
Vector Space Model ◽  
Nearest Neighbour ◽  
Inverse Document Frequency ◽  
Space Model ◽  
Document Frequency ◽  
Fold Cross Validation

Penelitian ini bertujuan untuk menemukan kombinasi dan urutan preprocessing dalam text mining yang paling maksimal untuk klasifikasi bidang jurnal berbahasa Indonesia berdasarkan judul dan abstraknya. Tahap-tahap preprocessing yang akan diterapkan terdiri dari case folding, stemming, stopwords removal, transformasi VSM (Vector Space Model), dan SMOTE. Namun, pengamatan tiap skenario berfokus pada stemming dan dua teknik stopwords removal, yaitu stopwords removal berbasis kamus, dan berbasis document frequency setelah melewati proses transformasi ke dalam bentuk VSM dengan pembobotan TF-IDF (Term Trequency–Inverse Document Frequency). Proses klasifikasi mengadopsi algoritma k-NN (K-Nearest Neighbour), yang menentukan kelas suatu data tes dengan melihat tetangga terdekatnya. Dalam penelitian ini, metrik untuk menemukan jarak tetangga terdekat adalah Cosine Similarity. Pengujian klasifikasi menggunakan 10-Fold Cross Validation untuk menghasilkan confusion matrix sebagai hasil akhir. Kinerja klasifikasi terbaik dicapai dengan persentase accuracy sebesar 72.91% dan precision mencapai 73,36%.

scholarly journals Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Peter Brown ◽  
Aik-Choon Tan ◽  
Mohamed A El-Esawi ◽  
Thomas Liehr ◽  
Oliver Blanck ◽  
...  
Keyword(s):  
Literature Search ◽  
Relevant Literature ◽  
Biomedical Literature ◽  
Medical Subject Headings ◽  
Document Similarity ◽  
Inverse Document Frequency ◽  
Research Fields ◽  
Experience Levels ◽  
Document Frequency ◽  
Systematic Biases

Abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

Poisson mixtures

1995 ◽  
Vol 1 (2) ◽  
pp. 163-190 ◽  
Author(s):  
Kenneth W. Church ◽  
William A. Gale
Keyword(s):  
Negative Binomial ◽  
Probability Distributions ◽  
Hidden Variables ◽  
Heterogeneous Structure ◽  
Text Compression ◽  
Inverse Document Frequency ◽  
Poisson Mixtures ◽  
Document Frequency ◽  
Wide Range ◽  
Better Than

AbstractShannon (1948) showed that a wide range of practical problems can be reduced to the problem of estimating probability distributions of words and ngrams in text. It has become standard practice in text compression, speech recognition, information retrieval and many other applications of Shannon's theory to introduce a “bag-of-words” assumption. But obviously, word rates vary from genre to genre, author to author, topic to topic, document to document, section to section, and paragraph to paragraph. The proposed Poisson mixture captures much of this heterogeneous structure by allowing the Poisson parameter θ to vary over documents subject to a density function φ. φ is intended to capture dependencies on hidden variables such genre, author, topic, etc. (The Negative Binomial is a well-known special case where φ is a Г distribution.) Poisson mixtures fit the data better than standard Poissons, producing more accurate estimates of the variance over documents (σ2), entropy (H), inverse document frequency (IDF), and adaptation (Pr(x ≥ 2/x ≥ 1)).

scholarly journals Efficient natural language classification algorithm for detecting duplicate unsupervised features

2021 ◽  
Vol 20 (3) ◽  
pp. 623-653
Author(s):  
Saud Altaf ◽  
Sofia Iqbal ◽  
Muhammad Waseem Soomro
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Short Term ◽  
Vocabulary Size ◽  
Language Understanding ◽  
Inverse Document Frequency ◽  
Classification Technique ◽  
Document Frequency ◽  
Text Features ◽  
Long Short Term Memory

This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.

scholarly journals Sistem Rekomendasi Produk Pena Eksklusif Menggunakan Metode Content-Based Filtering dan TF-IDF

2020 ◽  
Vol 5 (3) ◽  
pp. 229
Author(s):  
Mariani Widia Putri ◽  
Achmad Muchayan ◽  
Made Kamisutara
Keyword(s):  
Information Retrieval ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Brand Awareness ◽  
Product Knowledge ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Content Based Filtering

Sistem rekomendasi saat ini sedang menjadi tren. Kebiasaan masyarakat yang saat ini lebih mengandalkan transaksi secara online dengan berbagai alasan pribadi. Sistem rekomendasi menawarkan cara yang lebih mudah dan cepat sehingga pengguna tidak perlu meluangkan waktu terlalu banyak untuk menemukan barang yang diinginkan. Persaingan antar pelaku bisnis pun berubah sehingga harus mengubah pendekatan agar bisa menjangkau calon pelanggan. Oleh karena itu dibutuhkan sebuah sistem yang dapat menunjang hal tersebut. Maka dalam penelitian ini, penulis membangun sistem rekomendasi produk menggunakan metode Content-Based Filtering dan Term Frequency Inverse Document Frequency (TF-IDF) dari model Information Retrieval (IR). Untuk memperoleh hasil yang efisien dan sesuai dengan kebutuhan solusi dalam meningkatkan Customer Relationship Management (CRM). Sistem rekomendasi dibangun dan diterapkan sebagai solusi agar dapat meningkatkan brand awareness pelanggan dan meminimalisir terjadinya gagal transaksi di karenakan kurang nya informasi yang dapat disampaikan secara langsung atau offline. Data yang digunakan terdiri dari 258 kode produk produk yang yang masing-masing memiliki delapan kategori dan 33 kata kunci pembentuk sesuai dengan product knowledge perusahaan. Hasil perhitungan TF-IDF menunjukkan nilai bobot 13,854 saat menampilkan rekomendasi produk terbaik pertama, dan memiliki keakuratan sebesar 96,5% dalam memberikan rekomendasi pena.

scholarly journals Conceptual Sentiment Analysis Model

2018 ◽  
Vol 8 (4) ◽  
pp. 2358 ◽  
Author(s):  
Kranti Vithal Ghag ◽  
Ketan Shah
Keyword(s):  
Sentiment Analysis ◽  
Relative Frequency ◽  
Syntactic Structure ◽  
Research Work ◽  
Average Frequency ◽  
Semantic Structure ◽  
Analysis Model ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Improve Accuracy

<span>Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang &amp; Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.</span>

Food behaviours of Italian consumers at risk of poverty

2015 ◽  
Vol 117 (11) ◽  
pp. 2831-2848 ◽  
Author(s):  
Arianna Ruggeri ◽  
Anne Arvola ◽  
Antonella Samoggia ◽  
Vaiva Hendrixson
Keyword(s):  
At Risk ◽  
Design Methodology ◽  
Economic Status ◽  
Inverse Document Frequency ◽  
Content Type ◽  
Semantic Categories ◽  
Female Consumers ◽  
Document Frequency ◽  
Consumer Segment ◽  
Quantitative Analyses

Purpose – At a European level, Italy experiences one of the highest percentages of population at risk of poverty (AROP). However, studies on this consumer segment are scarce. The purpose of this paper is to investigate the food behaviours of Italian female consumers, distinguishing similarities and differences due to age and level of income. Design/methodology/approach – The investigation adopted an inductive approach in order to analyse and confirm the determinants of food behaviours. Data were collected through four focus groups. Data elaboration included content analyses with term frequency – inverse document frequency index and multidimensional scaling technique. Findings – The food behaviours of Italian female consumers are based on a common set of semantic categories and theoretical dimensions that are coherent with those applied by previous studies. The age of consumers impacts the relevance attributed to the categories and income contributes to the explanation of the conceptual relations among the categories that determine food behaviours. The approach to food of younger and mature consumers AROP is strongly driven by constraints such as price and time. The study did not confirm a link between a poor health attitude and low socio-economic status. Research limitations/implications – The outcomes achieved can be strengthened by quantitative analyses to characterise the relations occurring among the factors and dimensions that influence the food behaviours of consumers AROP. Originality/value – The study increases knowledge about Italian female consumers and provides an initial contribution to the analysis of the food behaviour of the population AROP.

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