Relevant bibliographies by topics / Term Frequency-Inverse Document Frequency (TF-IDF)

Contents

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

Academic literature on the topic 'Term Frequency-Inverse Document Frequency (TF-IDF)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 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 (TF-IDF).'

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 (TF-IDF)"

1

Mohammed, Mohannad T., and Omar Fitian Rashid. "Document retrieval using term term frequency inverse sentence frequency weighting scheme." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1478. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

Full text
Abstract:
The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents a
APA, Harvard, Vancouver, ISO, and other styles
2

Widianto, Adi, Eka Pebriyanto, Fitriyanti Fitriyanti, and Marna Marna. "Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity." Journal of Dinda : Data Science, Information Technology, and Data Analytics 4, no. 2 (2024): 149–53. http://dx.doi.org/10.20895/dinda.v4i2.1589.

Full text
Abstract:
Document similarity is a fundamental task in natural language processing and information retrieval, with applications ranging from plagiarism detection to recommendation systems. In this study, we leverage the term frequency-inverse document frequency (TF-IDF) to represent documents in a high-dimensional vector space, capturing their unique content while mitigating the influence of common terms. Subsequently, we employ the cosine similarity metric to measure the similarity between pairs of documents, which assesses the angle between their respective TF-IDF vectors. To evaluate the effectivenes
APA, Harvard, Vancouver, ISO, and other styles
3

Mohannad, T. Mohammed, and Fitian Rashid Omar. "Document retrieval using term frequency inverse sentence frequency weighting scheme." Document retrieval using term frequency inverse sentence frequency weighting scheme 31, no. 3 (2023): 1478–85. https://doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

Full text
Abstract:
The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing
APA, Harvard, Vancouver, ISO, and other styles
4

Yulita, Winda, Meida Cahyo Untoro, Mugi Praseptiawan, Ilham Firman Ashari, Aidil Afriansyah, and Ahmad Naim Bin Che Pee. "Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity." Scientific Journal of Informatics 10, no. 2 (2023): 93–104. http://dx.doi.org/10.15294/sji.v10i2.42209.

Full text
Abstract:
Purpose: In the learning process, most of the tests to assess learning achievement have been carried out by providing questions in the form of short answers or essay questions. The variety of answers given by students makes a teacher have to focus on reading them. This scoring process is difficult to guarantee quality if done manually. In addition, each class is taught by a different teacher, which can lead to unequal grades obtained by students due to the influence of differences in teacher experience. Therefore the purpose of this study is to develop an assessment of the answers. Automated s
APA, Harvard, Vancouver, ISO, and other styles
5

Ni'mah, Ana Tsalitsatun, and Agus Zainal Arifin. "Perbandingan Metode Term Weighting terhadap Hasil Klasifikasi Teks pada Dataset Terjemahan Kitab Hadis." Rekayasa 13, no. 2 (2020): 172–80. http://dx.doi.org/10.21107/rekayasa.v13i2.6412.

Full text
Abstract:
Hadis adalah sumber rujukan agama Islam kedua setelah Al-Qur’an. Teks Hadis saat ini diteliti dalam bidang teknologi untuk dapat ditangkap nilai-nilai yang terkandung di dalamnya secara pegetahuan teknologi. Dengan adanya penelitian terhadap Kitab Hadis, pengambilan informasi dari Hadis tentunya membutuhkan representasi teks ke dalam vektor untuk mengoptimalkan klasifikasi otomatis. Klasifikasi Hadis diperlukan untuk dapat mengelompokkan isi Hadis menjadi beberapa kategori. Ada beberapa kategori dalam Kitab Hadis tertentu yang sama dengan Kitab Hadis lainnya. Ini menunjukkan bahwa ada beberapa
APA, Harvard, Vancouver, ISO, and other styles
6

Priyanka, Mesariya, and Madia Nidhi. "Document Ranking using Customizes Vector Method." International Journal of Trend in Scientific Research and Development 1, no. 4 (2017): 278–83. https://doi.org/10.31142/ijtsrd125.

Full text
Abstract:
Information retrieval IR system is about positioning reports utilizing clients question and get the important records from extensive dataset. Archive positioning is fundamentally looking the pertinent record as per their rank. Document ranking is basically search the relevant document according to their rank. Vector space model is traditional and widely applied information retrieval models to rank the web page based on similarity values. Term weighting schemes are the significant of an information retrieval system and it is query used in document ranking. Tf idf ranked calculates the term weig
APA, Harvard, Vancouver, ISO, and other styles
7

Christian, Hans, Mikhael Pramodana Agus, and Derwin Suhartono. "Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF)." ComTech: Computer, Mathematics and Engineering Applications 7, no. 4 (2016): 285. http://dx.doi.org/10.21512/comtech.v7i4.3746.

Full text
Abstract:
The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online sour
APA, Harvard, Vancouver, ISO, and other styles
8

Setiawan, Gede Herdian, and I. Made Budi Adnyana. "Improving Helpdesk Chatbot Performance with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity Models." Journal of Applied Informatics and Computing 7, no. 2 (2023): 252–57. http://dx.doi.org/10.30871/jaic.v7i2.6527.

Full text
Abstract:
Helpdesk chatbots are growing in popularity due to their ability to provide help and answers to user questions quickly and effectively. Chatbot development poses several challenges, including enhancing accuracy in understanding user queries and providing relevant responses while improving problem-solving efficiency. In this research, we aim to enhance the accuracy and efficiency of the Helpdesk Chatbot by implementing the Term Frequency-Inverse Document Frequency (TF-IDF) model and the Cosine Similarity algorithm. The TF-IDF model is a method used to measure the frequency of words in a documen
APA, Harvard, Vancouver, ISO, and other styles
9

Tamardina, Fadhilla Atansa, Hasbi Yasin, and Dwi Ispriyanti. "ANALISIS SENTIMEN REVIEW APLIKASI CRYPTOCURRENCY MENGGUNAKAN ALGORITMA MAXIMUM ENTROPY DENGAN METODE PEMBOBOTAN TF, TF-IDF DAN BINARY." Jurnal Gaussian 11, no. 1 (2022): 1–10. http://dx.doi.org/10.14710/j.gauss.v11i1.34004.

Full text
Abstract:
Pandemi COVID-19 yang belum berhenti menyebabkan kondisi ekonomi Indonesia kian memburuk. Masyarakat yang terkena dampak pemotongan upah akibat pandemi harus mencari cara untuk mendapatkan pendapatan pasif. Salah satu cara untuk mendapatkan hal tersebut adalah berinvestasi. Cryptocurrency adalah salah satu instrumen investasi berbasis aplikasi yang memiliki return tinggi. Aplikasi Pintu adalah aplikasi pertama yang menyediakan fasilitas mobile apps pada penggunanya. Aplikasi yang dirilis pada tahun 2020 ini sudah memiliki banyak ulasan yang diberikan oleh penggunanya. Ulasan ini dibutuhkan unt
APA, Harvard, Vancouver, ISO, and other styles
10

Tama, Fauzaan Rakan, and Yuliant Sibaroni. "Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method." JINAV: Journal of Information and Visualization 4, no. 1 (2023): 70–78. http://dx.doi.org/10.35877/454ri.jinav1525.

Full text
Abstract:
The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in th
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Term Frequency-Inverse Document Frequency (TF-IDF)"

1

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

Book chapters on the topic "Term Frequency-Inverse Document Frequency (TF-IDF)"

1

Zhang, Jincheng, Thada Jantakoon, and Potsirin Limpinan. "Multiple Novel Algorithms Based on TF-IDF and Inverse Document Frequency, Experimented with Text Data in the Education Field." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88042-1_23.

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

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
3

Redd Andrew M., Gundlapalli Adi V., Divita Guy, Tran Le-Thuy, Pettey Warren B.P., and Samore Matthew H. "Comparison of Grouping Methods for Template Extraction from VA Medical Record Text." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-781-8-136.

Full text
Abstract:
We investigate options for grouping templates for the purpose of template identification and extraction from electronic medical records. We sampled a corpus of 1000 documents originating from Veterans Health Administration (VA) electronic medical record. We grouped documents through hashing and binning tokens (Hashed) as well as by the top 5% of tokens identified as important through the term frequency inverse document frequency metric (TF-IDF). We then compared the approaches on the number of groups with 3 or more and the resulting longest common subsequences (LCSs) common to all documents in
APA, Harvard, Vancouver, ISO, and other styles
4

Krupalija, Ehlimana, Emir Cogo, Damir Pozderac, Aya Ali Al Zayat, and Ingmar Bešić. "Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction." In Machine Learning and Artificial Intelligence. IOS Press, 2023. http://dx.doi.org/10.3233/faia230774.

Full text
Abstract:
Cause-effect graphs (CEGs) are usually applied for black-box testing of complex industrial systems. The specification process is time-consuming and can result in many errors. In this work, machine learning methods were applied for predicting the feasibility of CEG elements. All information was extracted from graphs contained in CEGSet, a dataset of CEGs. The data was converted to two different formats. The Boolean features format represents relations as separate data rows, whereas the Term-Frequency times Inverse-Document-Frequency (TF-IDF) format represents graphs as data rows. Eight machine
APA, Harvard, Vancouver, ISO, and other styles
5

Ahmed, Wesam, Noura A. Semary, Khalid Amin, and Mohamed Hammad. "Hyperparameter Optimization of Machine Learning Models Using Grid Search for Twitter Sentiment Analysis." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7011-7.ch021.

Full text
Abstract:
Twitter has emerged as a significant social media platform and has garnered significant interest from sentiment analysis researchers. Text mining is an active subfield that includes Twitter sentiment analysis (TSA) research. TSA is the term used to describe the utilization of algorithms to analyze the subjective nature of Twitter data, which includes its sentiments and opinions. The extraction of inferences from user interactions is facilitated by machine learning (ML) approaches. A wide range of machine learning methodologies are employed to analyze emotions. This research compares four super
APA, Harvard, Vancouver, ISO, and other styles
6

Pişginel, Osman, and Gülşah Güler. "Yapay Zeka İle Sınavın Şifresi: LGS Fen Bilimleri Sorularının Sözcük Temelli İncelenmesi." In Eğitimde Teknoloji Entegrasyonu: Yapay Zeka ve Dijitalleşme Perspektifleri. Özgür Yayınları, 2025. https://doi.org/10.58830/ozgur.pub773.c3205.

Full text
Abstract:
Bu çalışma, 2018–2024 yılları arasında uygulanan LGS Fen Bilimleri sorularını sözcük temelli doğal dil işleme (NLP) teknikleriyle analiz ederek sınavın kavramsal yapısını ortaya koymayı amaçlamaktadır. 120 soruluk veri seti, Zemberek-NLP kütüphanesi aracılığıyla işlenmiş; durdurucu kelimeler temizlenmiş ve sözcük türlerine ayrılmıştır. Ardından, kelime frekansı, TF-IDF (Term Frequency–Inverse Document Frequency) analizi, kavram kümelenmesi ve kelime bulutu teknikleri uygulanmıştır. Analiz sonuçları, LGS sorularının büyük ölçüde 8. sınıf kazanımlarına dayandığını ve özellikle Canlılar ve Yaşam
APA, Harvard, Vancouver, ISO, and other styles
7

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
8

Selvi, D. Thamarai, S. Kalaiselvi, V. Anitha, S. Santhi, V. Gomathi, and Sathish Kumar Sekar. "A Sentimental Analysis of Legal Documents Using Mask Attention BERT Networks." In Advances in Web Technologies and Engineering. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7868-7.ch010.

Full text
Abstract:
Legal systems can function more efficiently by processing cases faster and having a higher case clearance rate when complex legal texts are automatically analysed for logical patterns. The most crucial task in doing this is classifying sentences in legal documents automatically based on their content. This chapter suggests a deep learning model for sentiment analysis-based legal text analysis and judgment generation. The transformer model is an innovative encoder-decoder that uses self-awareness to analyse speech patterns which runs noticeably quicker and allows for parallel processing. In thi
APA, Harvard, Vancouver, ISO, and other styles
9

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
10

Idalisa, Nur, Muhammad Hazwan Mohd Hazhar, Norliana Muslim, and Nur Lyana Shahfiqa Albashah. "Restaurant Recommendation System in Malaysia Using Machine Learning Approach." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241357.

Full text
Abstract:
People frequently struggle to make decisions when faced with a wider range of possibilities, especially when selecting a dining restaurant. To address this issue, a recommendation system can assist by analyzing user preferences and previous dining experiences to offer personalized suggestions. This research aims to develop a restaurant recommendation system for Malaysian customers using a machine-learning approach. The study focuses on Non-negative Matrix Factorization (NMF), Probability Matrix Factorization (PMF), Principal Component Analysis (PCA), and Singular Value Decomposition (SVD) appr
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Term Frequency-Inverse Document Frequency (TF-IDF)"

1

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
2

Hakim, Ari Aulia, Alva Erwin, Kho I. Eng, Maulahikmah Galinium, and Wahyu Muliady. "Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach." In 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2014. http://dx.doi.org/10.1109/iciteed.2014.7007894.

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

Khunruksa, Sahussawud, and Somkiat Wangsiripitak. "Learning Extended Term Frequency-Inverse Document Frequency (TF-IDF++) for Depression Screening From Sentences in Thai Blog Post." In 2023 8th International Conference on Business and Industrial Research (ICBIR). IEEE, 2023. http://dx.doi.org/10.1109/icbir57571.2023.10147692.

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

Rodrigues da Silva, Mônica, Anita Maria da Rocha Fernandes, and Guilherme Falcão da Silva Campos. "Implementação de Chatbot para Aprimorar a Comunicação com Usuários de Serviços Públicos." In Computer on the Beach. Universidade do Vale do Itajaí, 2021. http://dx.doi.org/10.14210/cotb.v12.p480-482.

Full text
Abstract:
This paper describes the study and implementation of a chatbot tohelp users of public services with their most frequent questions.The chatbot was developed based on the TF-IDF (Term Frequency -Inverse Document Frequency) model, using the Python languageand the Django framework. Functions such as registration of questionsand answers, were implemented using the Java language withAPI Restful and Spring Boot, and the MongoDB database. Finally,to enable the interaction of internal and external users with thesystem, front ends were built using the TypeScript language andthe Angular platform. The sys
APA, Harvard, Vancouver, ISO, and other styles
5

Zen, Bita Parga, Irwan Susanto, Khofifah Putriyani, and Sintiya. "Automatic document classification for tempo news articles about covid 19 based on term frequency, inverse document frequency (TF-IDF), and Vector Space Model (VSM)." In THE 8TH INTERNATIONAL CONFERENCE ON TECHNOLOGY AND VOCATIONAL TEACHERS 2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0212036.

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

Costa, José Alfredo, and Nielsen Dantas. "Análise Comparativa de Embeddings Jurídicos aplicados a Algoritmos de Clustering." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-181.

Full text
Abstract:
Text clustering analysis plays an important role in the organization and comprehension of extensive amounts of textual data. By grouping semantically similar documents into coherent categories, or clusters, it is possible to extract pertinent information and the unearthing of latent patterns embedded within the text. Text clustering enables a deeper understanding of the underlying structure and relationships within textual data, therefore, unveiling patterns and thematic trends. This paper aims to evaluate the impact of different text embeddings in the task of clustering Brazilian legal docume
APA, Harvard, Vancouver, ISO, and other styles
7

Schofield, Matthew, Gulsum Alicioglu, Russell Binaco, et al. "Convolutional Neural Network for Malware Classification Based on API Call Sequence." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110106.

Full text
Abstract:
Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application
APA, Harvard, Vancouver, ISO, and other styles
8

Fakhrurroja, Hanif, Tanrida Utari, Andy Victor, and Oka Mahendra. "Talk to Me: Artificial Intelligence “Virtual Friend” for Depression Sufferers Using Term Frequency-Inverse Document Frequency (TF-IDF) and Finite State Machine Method." In 2022 International Conference on ICT for Smart Society (ICISS). IEEE, 2022. http://dx.doi.org/10.1109/iciss55894.2022.9915089.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Jaiswal, Saurabh, and Mr Vivek Rai. "ENHANCING GENERALIZATION IN FAKE NEWS DETECTION: A COMPARATIVE EVALUATION OF NAIVE BAYES AND RANDOM FOREST APPROACHES." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.28.

Full text
Abstract:
In our digitally interconnected era, the rampant spread of fake news stands as a formidable challenge, jeopardizing informed decision-making, eroding public trust, and undermining democratic processes. This research addresses this pressing issue by introducing an ensemble machine learning model designed for the classification of fake news. As the dissemination of news becomes increasingly challenging amid the explosion of online information, the study delves into the application of Naive Bayes Classification models with bag of words features, and an additional model employing tf-idf instead of
APA, Harvard, Vancouver, ISO, and other styles
10

"Umeed: VR Game Using NLP Models and Latent Semantic Analysis for Conversation Therapy for People with Speech Disorders." In 4th International Conference on NLP Trends & Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131408.

Full text
Abstract:
UmeedVR aims to create a conversational therapy VR game using natural language processing for patients with Speech Disorders like Autism or Aphasia. This study developed 5 psychological task sets and 3 environments via Maya and Unity. The Topic-Modeling AI, employing 25 live participants' recordings and 980+ TwineAI datasets, generated initial VR grading with a coherence score averaging 6.98 themes in 5-minute conversations across scenarios, forming a foundation for enhancements. Employing latent semantic analysis (gensimcorpus Python) and Term-Frequency-Inverse Document-Frequency (TF-IDF), gr
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Term Frequency-Inverse Document Frequency (TF-IDF)' 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