Relevant bibliographies by topics / Term Frequency-Inverse Document Frequency vectorization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Term Frequency-Inverse Document Frequency vectorization'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Term Frequency-Inverse Document Frequency vectorization.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Term Frequency-Inverse Document Frequency vectorization"

1

Shafah, Ali, Ahmed Suleiman, and Samira Alshafah. "Impact Feature Vectorization Methods on Arabic Large Data Using Logistic Regression Classification." University of Zawia Journal of Engineering Sciences and Technology 1, no. 1 (2024): 22–29. http://dx.doi.org/10.26629/uzjest.2023.03.

Full text
Abstract:
The process of assigning text documents to a predetermined set of categories is known as text categorization. The objective of this study is to present experimental assessments of various feature vectorization methods for the purpose of categorizing a large Arabic corpus using a logistic regression classifier. N-Gram, Bag of Words, and Term Frequency–Inverse Document Frequency are these methods. A corpus of around 111,000 Arabic documents was utilized, which was split up into five categories: news, sports, culture, economics, and varied. Each method's experimental findings were assessed using
APA, Harvard, Vancouver, ISO, and other styles
2

M, Ms AISHWARYA LAKSHMI. "MOVIE SIMILARITY FROM PLOT SUMMARIES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33647.

Full text
Abstract:
This project focuses on developing a Python application to analyze and measure the similarity between movie plot summaries. The goal is to provide a tool that can assist in identifying similarities between movies based on their storyline, enabling users to discover related movies or recommend similar ones.The project utilizes natural language processing (NLP) techniques, particularly text preprocessing, vectorization, and similarity metrics, to achieve its objectives. First, it preprocesses the plot summaries by removing stop words, punctuation, and performing stemming or lemmatization to norm
APA, Harvard, Vancouver, ISO, and other styles
3

Bhargavi, A. D. "Comparative Study of Static and Contextual Text Vectorization for Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 484–88. https://doi.org/10.22214/ijraset.2025.73045.

Full text
Abstract:
Sentiment analysis, a core task in Natural Language Processing (NLP), relies heavily on effective text representation techniques to capture semantic and syntactic nuances. This study presents a comparative analysis of widely-used vectorization methods—Bag of Words (BoW), Term Frequency–Inverse Document Frequency (TF-IDF), Word2Vec, GloVe, BERT, and RoBERTa—in the context of sentiment classification. Using the IMDb movie reviews dataset, each method is evaluated based on classification performance, using accuracy and F1-score as primary metrics. Results demonstrate that while deep contextual em
APA, Harvard, Vancouver, ISO, and other styles
4

Aminu, Bunyaminu Khalid, Dr Anupa Sinha, and Ahmad Mustapha. "PhishGuard: A Machine Learning Framework for Windows-Specific Phishing Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 81–89. https://doi.org/10.22214/ijraset.2025.70104.

Full text
Abstract:
Abstract: Phishing remains one of the most prevalent and evolving cybersecurity threats, exploiting humanvulnerabilities through deceptive digital communication. This study proposes a dynamic, Windows-specific phishing detection model leveraging Random Forest machine learning techniques. By integrating Term Frequency–Inverse Document Frequency (TF-IDF) vectorization with structured email features, the model classifies phishing and legitimate emails with high accuracy. Using secondary data and publicly available datasets, the model achieved a classification accuracy of 98.31% and demonstrated b
APA, Harvard, Vancouver, ISO, and other styles
5

Winahyu, Sri Kusuma, Fawwaz Zaini Ahmad, Achril Zalmansyah, et al. "Sentence Classification Using Machine Learning and Word Embedding: An Innovation in Indonesian Language Learning." Journal of Language Teaching and Research 16, no. 4 (2025): 1225–39. https://doi.org/10.17507/jltr.1604.17.

Full text
Abstract:
In applied linguistics, writing assessment examines language learning. There are various genres in writing, but the evaluation always includes a syntactic component or sentence structure. This research focuses on classifying sentence structure in the Indonesian language using the Random Forest Classifier algorithm on five different experiment models, which are trained using different vectorization techniques, including bag of word (BoW), hashing, Term Frequency-Inverse Document Frequency (TF-IDF), CBoW, and skipgram vectorizers. The results showed that the accuracy of the models varied signifi
APA, Harvard, Vancouver, ISO, and other styles
6

Rahman, Abdur, Abu Nayem, and Saeed Siddik. "Non-Functional Requirements Classification Using Machine Learning Algorithms." International Journal of Intelligent Systems and Applications 15, no. 3 (2023): 56–69. http://dx.doi.org/10.5815/ijisa.2023.03.05.

Full text
Abstract:
Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functio
APA, Harvard, Vancouver, ISO, and other styles
7

Anuradha, Surabhi, Pothabathula Naga Jyothi, Surabhi Sivakumar, and Martha Sheshikala. "RecommendRift: a leap forward in user experience with transfer learning on netflix recommendations." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1218. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp1218-1225.

Full text
Abstract:
In today’s fast-paced lifestyle, streaming movies and series on platforms like Netflix is a valued recreational activity. However, users often spend considerable time searching for the right content and receive irrelevant recommendations, particularly when facing the “cold start problem” for new users. This challenge arises from existing recommender systems relying on factors like casting, title, and genre, using term frequency-inverse document frequency (TF-IDF) for vectorization, which prioritizes word frequency over semantic meaning. To address this, an innovative recommender system conside
APA, Harvard, Vancouver, ISO, and other styles
8

Tao, Chang, Shaoming Zheng, Shuhong Wang, et al. "On Defect Grading for the Relay Protection Devices Based on TF-IDF Assignment and Simple Classifiers." Journal of Physics: Conference Series 2433, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2433/1/012023.

Full text
Abstract:
Abstract Accurate grading of relay protection device (RPD) defects can improve the maintenance and reliability of RPD to ensure the safety of power grid. Based on the text record of defects of RPDs in a regional power grid and the defect text dictionary, this paper analyses the defect grading method with Term Frequency-Inverse Document Frequency (TF-IDF) assignment method and simple classifiers. The details are as follows: firstly, the construction of relay protection devices defect dictionary is introduced. Secondly, the vectorization text of relay protection devices defect is formed; combine
APA, Harvard, Vancouver, ISO, and other styles
9

Lu, Jiaxin. "Text vectorization in sentiment analysis: A comparative study of TF-IDF and Word2Vec from Amazon Fine Food Reviews." ITM Web of Conferences 70 (2025): 03001. https://doi.org/10.1051/itmconf/20257003001.

Full text
Abstract:
Sentiment analysis is a practical tool for marketing and branding teams. Companies can collect and analyze opinions or reviews from social media platforms, blog posts, and other numerous forums. It may help them acquire positive feedback to reinforce strengths or identify negative emotions to make improvements. The research is to compare two text vectorization methods in opinion mining: Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, using Amazon Fine Food Reviews dataset. This study will use these two methods to vectorize preprocessed text data and also input the vectorized d
APA, Harvard, Vancouver, ISO, and other styles
10

Surabhi, Anuradha Pothabathula Naga Jyothi Surabhi Sivakumar Martha Sheshikala. "RecommendRift: a leap forward in user experience with transfer learning on netflix recommendations." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1218–25. https://doi.org/10.11591/ijeecs.v36.i2.pp1218-1225.

Full text
Abstract:
In today’s fast-paced lifestyle, streaming movies and series on platforms like  Netflix is a valued recreational activity. However, users often spend considerable time searching for the right content and receive irrelevant recommendations, particularly when facing the “cold start problem” for new users. This challenge arises from existing recommender systems relying on factors like casting, title, and genre, using term frequency-inverse document frequency (TF-IDF) for vectorization, which prioritizes word frequency over semantic meaning. To address this, an innovative re
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Term Frequency-Inverse Document Frequency vectorization"

1

Sullivan, Daniel Edward. "Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/80572.

Full text
Abstract:
This research addresses the problem, can unsupervised learning generate a representation that improves on the commonly used term frequency-inverse document frequency (TF-IDF ) representation by capturing semantic relations? The analysis measures the quality of sentence classification using term TF-IDF representations, and finds a practical upper limit to precision and recall in a biomedical text classification task (F1-score of 0.85). Arguably, one could use ontologies to supplement TF-IDF, but ontologies are sparse in coverage and costly to create. This prompts a correlated question: can uns
APA, Harvard, Vancouver, ISO, and other styles
2

Regard, Viktor. "Studying the effectiveness of dynamic analysis for fingerprinting Android malware behavior." Thesis, Linköpings universitet, Databas och informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163090.

Full text
Abstract:
Android is the second most targeted operating system for malware authors and to counter the development of Android malware, more knowledge about their behavior is needed. There are mainly two approaches to analyze Android malware, namely static and dynamic analysis. Recently in 2017, a study and well labeled dataset, named AMD (Android Malware Dataset), consisting of over 24,000 malware samples was released. It is divided into 135 varieties based on similar malicious behavior, retrieved through static analysis of the file classes.dex in the APK of each malware, whereas the labeled features wer
APA, Harvard, Vancouver, ISO, and other styles
3

Fan, Fang-Syuan, and 范芳瑄. "Classified Term Frequency-Inverse Document Frequency technique applied to school regulationsClassified Term Frequency-Inverse Document Frequency technique applied to school regulations." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6hb936.

Full text
Abstract:
碩士<br>國立中央大學<br>資訊工程學系在職專班<br>107<br>This study combines Term Frequency-Inverse Document Frequency technique with compatibility and applies it to the “Regulations of National Central University and Extensions of Off-campus Regulations” and establishes them on the cloud platform for tax classification. Term Frequency-Inverse Document Frequency technique can only present one type of measurement and quantitative method and is not capable of presenting diverse selection. Therefore, through the combination of compatibility, Cosine Similarity, Hierarchical Clustering and other techniques, a regulati
APA, Harvard, Vancouver, ISO, and other styles
4

Lin, Jun-liang, and 林俊良. "A New Auto Document Category System by Using Google N-gram and Probability based Term Frequency and Inverse Category Frequency." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/20409545542311421955.

Full text
Abstract:
碩士<br>國立高雄第一科技大學<br>資訊管理研究所<br>100<br>The electronic documents between companies and organizations are growing fast. Automatic classification is an important issue of information service and knowledge management. Keywords are the smallest units to present the document. Therefore, almost each part of document automation processing such as knowledge mining, automatic filtering, automatic summarization, event tracking, or concept retrieval etc., have to retrieve keywords from documents first, and then proceed with analytical processing. We propose the N-gram Segmentation Algorithm (NSA) in this s
APA, Harvard, Vancouver, ISO, and other styles
5

Costa, Joao Mario Goncalves da. "Classificação automática de páginas web usando features visuais." Master's thesis, 2014. http://hdl.handle.net/10316/40401.

Full text
Abstract:
Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra<br>The world of Internet grows up every day. There are a large number of web pages actives at this moment and more are released every day. It is impossible to perform the web page classification manually. It was already developed several approaches in this area. Most of them only use the text information contained in the web pages, ignoring the visual content of them. This work shows that the visual content can improve the accuracies o
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Term Frequency-Inverse Document Frequency vectorization"

1

Shi, Feng. Learn About Term Frequency–Inverse Document Frequency in Text Analysis in R With Data From How ISIS Uses Twitter Dataset (2016). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526489012.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Shi, Feng. Learn About Term Frequency–Inverse Document Frequency in Text Analysis in Python With Data From How ISIS Uses Twitter Dataset (2016). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498038.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Term Frequency-Inverse Document Frequency vectorization"

1

Kumar, Mukesh, and Renu Vig. "Term-Frequency Inverse-Document Frequency Definition Semantic (TIDS) Based Focused Web Crawler." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29216-3_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Quan, Do Viet, and Phan Duy Hung. "Application of Customized Term Frequency-Inverse Document Frequency for Vietnamese Document Classification in Place of Lemmatization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68154-8_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rajagukguk, Novita, I. Putu Eka Nila Kencana, and I. G. N. Lanang Wijaya Kusuma. "Application of Term Frequency - Inverse Document Frequency in The Naive Bayes Algorithm For ChatGPT User Sentiment Analysis." In Advances in Computer Science Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-413-6_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Muppala, Gurusai, and T. Devi. "Comparison of accuracy for rapid automatic keyword extraction algorithm with term frequency inverse document frequency to recast giant text into charts." In Applications of Mathematics in Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003606659-100.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Fattahi, Jaouhar, Mohamed Mejri, and Marwa Ziadia. "Extreme Gradient Boosting for Cyberpropaganda Detection." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210012.

Full text
Abstract:
Propaganda, defamation, abuse, insults, disinformation and fake news are not new phenomena and have been around for several decades. However, with the advent of the Internet and social networks, their magnitude has increased and the damage caused to individuals and corporate entities is becoming increasingly greater, even irreparable. In this paper, we tackle the detection of text-based cyberpropaganda using Machine Learning and NLP techniques. We use the eXtreme Gradient Boosting (XGBoost) algorithm for learning and detection, in tandem with Bag-of-Words (BoW) and Term Frequency-Inverse Docum
APA, Harvard, Vancouver, ISO, and other styles
6

Raikoti, Sharanabasappa, C. Arunabala, S. Sakthi Vinayagam, and G. Manikandan. "Machine Learning in Understanding Public Perceptions and Expectations in Accuracy of Automotive Safety." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0442-7.ch024.

Full text
Abstract:
This work focuses on the application of ML method in conceiving the people's perception and expectations over the efficacy of automotive safety systems. Conducted with topological data analysis, the research applies Sentiment Analysis with BERT and Apache Spark with NLP Libraries to process big textual data from surveys, social media, and online reviews. The kind of data preprocessing involves Text Vectorization using the BERT Tokenizer to maintain the context information. BF and MF are applied using the TF-IDF (Term Frequency-Inverse Document Frequency) to identify the leading terms motivatin
APA, Harvard, Vancouver, ISO, and other styles
7

"Term Frequency by Inverse Document Frequency." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_3784.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Nagaraj, Nagendra, and Chandra J. "Sentence Classification using Machine Learning with Term Frequency–Inverse Document Frequency with N-Gram." In New Frontiers in Communication and Intelligent Systems. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-35.

Full text
Abstract:
Automatic text classification has proven to be a vital method for managing and processing a very large text area—the volume of digital materials that is spreading and growing on a daily basis. In general, text plays an important role in classifying, extracting, and summarizing information, searching for text, and answering questions. This paper demonstrates machine learning techniques are used for the text classification process..And also, with the vast rapid growth of text analysis in all areas, the demand for automatic text classification has widely improved by day by day. The pattern of tex
APA, Harvard, Vancouver, ISO, and other styles
9

Uwiragiye, Eugene, and Kristen L. Rhinehardt. "Identification of RNA Oligonucleotide and Protein Interactions Using Term Frequency Inverse Document Frequency and Random Forest." In Oligonucleotides - Overview and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.108819.

Full text
Abstract:
The interaction between protein and Ribonucleic Acid (RNA) plays crucial roles in many biological aspects such as gene expression, posttranscriptional regulation, and protein synthesis. However, the experimental screening of protein-RNA binding affinity is laborious and time-consuming, there is a pressing desire of accurate and reliable computational approaches. In this study, we proposed a novel method to predict that interaction based on both sequences of protein and RNA. The Random Forest was trained and tested on a combination of benchmark datasets and the term frequency–inverse document f
APA, Harvard, Vancouver, ISO, and other styles
10

You, Zi-Hung, Ya-Han Hu, Chih-Fong Tsai, and Yen-Ming Kuo. "Integrating Feature and Instance Selection Techniques in Opinion Mining." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch042.

Full text
Abstract:
Opinion mining focuses on extracting polarity information from texts. For textual term representation, different feature selection methods, e.g. term frequency (TF) or term frequency–inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification, however, a selected training set may contain noisy documents (or outliers), which can degrade the classification performance. To solve this problem, instance selection can be adopted to filter out unrepresentative training documents. Therefore, this article investigates the opinion mining performance associated
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Term Frequency-Inverse Document Frequency vectorization"

1

Lubis, Hasby Sahendri, Mahyuddin K. M. Nasution, and Amalia Amalia. "Performance of Term Frequency - Inverse Document Frequency and K-Means in Government Service Identification." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10748106.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wen, Xiaojiao. "Student Grade Prediction and Classification based on Term Frequency-Inverse Document Frequency with Random Forest." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796287.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bharattej R, Rana Veer Samara Sihman, Prashanth V, Haideer Alabdeli, Sunaina Sangeet Thottan, and S. Ananthi. "Modified Term Frequency and Inverse Document Frequency with Optimized Deep Learning Algorithm based Fake News Detection." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934578.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Fengjuan. "Japanese Dependency Analysis using Multi-Kernel Support Vector Machine based on Term Frequency and Inverse Document Frequency." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860205.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Islavath, Srinivas, and C. Rohith Bhat. "Uniform Resource Locator Phishing in Real Time Scenario Predicted Using Novel Term Frequency-Inverse Document Frequency +N Gram in Comparison with Support Vector Machine Algorithm." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725919.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lumintu, Ida. "Content-Based Recommendation Engine Using Term Frequency-Inverse Document Frequency Vectorization and Cosine Similarity: A Case Study." In 2023 IEEE 9th Information Technology International Seminar (ITIS). IEEE, 2023. http://dx.doi.org/10.1109/itis59651.2023.10420137.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Barros, Filipe M. C., Cleison D. Silva, Igor R. M. Silva, Victor S. Martins, and Antonio J. S. Araújo. "Machine Learning Algorithms Applied on Classification of Processes for Conciliation on Brazilian Labour Judiciary." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234189.

Full text
Abstract:
The Labour Judiciary ensures protection and justice in labour relations, resolving conflicts such as unfair dismissals and wage delays. Artificial intelligence emerges to expedite legal activities, assisting in dealing with the increasing case load in the Judiciary over the past years. In labor dispute resolution, conciliation is a recommended solution, offering speed and cost reduction. In this sense, this study proposes to evaluate models to predict the success of labor cases being resolved through conciliation. The dataset used to generate the models considered in this study consists of ini
APA, Harvard, Vancouver, ISO, and other styles
8

Danabal, Tharunya, Neethi Sarah John, Abhijeet Pramod Ghawade, and Pranjal Padharinath Ahire. "Cognitive HSE Risk Prediction and Notification Tool Based on Natural Language Processing." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205877-ms.

Full text
Abstract:
Abstract The focus of this work is on developing a cognitive tool that predicts the most frequent HSE hazards with the highest potential severity levels. The tool identifies these risks using a natural language processing algorithm on HSE leading and lagging indicator reports submitted to an oilfield services company’s global HSE reporting system. The purpose of the tool is to prioritize proactive actions and provide focus to raise workforce awareness. A natural language processing algorithm was developed to identify priority HSE risks based on potential severity levels and frequency of occurr
APA, Harvard, Vancouver, ISO, and other styles
9

Baena-Garcia, Manuel, Jose M. Carmona-Cejudo, Gladys Castillo, and Rafael Morales-Bueno. "TF-SIDF: Term frequency, sketched inverse document frequency." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121796.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Anil, Nagar Kiran, and Bobbin Preet Kaur. "Term Frequency Inverse Document Frequency based Sentiment Analysis using Machine Learning Approaches." In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2023. http://dx.doi.org/10.1109/icdsaai59313.2023.10452661.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Term Frequency-Inverse Document Frequency vectorization' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography