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1 week ago Vector space modeling is essential for any NLP problem. I used ‘Tf-Idf’. See more
3 days ago WEB The BBC News Classification dataset is used in this project for training and testing the models. The dataset comprises of 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech. It is parted into two sets: 1) train set with 1490 records, and 2) test set with 735 records.
6 days ago WEB The dataset utilized is an open-source dataset from Kaggle BBC news classification. Furthermore, the models are assessed using standard performance metrics like accuracy, cross-entropy loss, and time required for training. The results depict that both the models perform nearly the same by achieving a training accuracy of 100% and a validation ...
1 week ago WEB Jan 8, 2023 · In the BBC News Classification Project, we are building a predictive model to evaluate the various news records and classify them accordingly with the help of some parameters. The parameters into consideration are the various headlines with their respective categories. After cleaning and preprocessing the dataset using NLP …
4 days ago WEB Explore and run machine learning code with Kaggle Notebooks | Using data from BBC articles fulltext and category
1 week ago WEB BBC News Text Classification - Medium. 1 week ago The text needs to be transformed to vectors so as the algorithms will be able make predictions. In this case it will be used the Term Frequency — Inverse Document Frequency (TFIDF) weight to evaluate how important a word is to a document in a collection of documents.
2 days ago WEB Mar 19, 2023 · This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for …
2 days ago WEB The BBC News dataset comprises approximately 2,225 news articles published by BBC News in the early 2000s. Each article is associated with one of five categories: business, entertainment, politics, sport, and tech. By examining these articles, we can gain a deeper understanding of how news content is distributed across various domains and ...
1 week ago WEB 3. DATA 3.1 Text Classification For this research, we used the BBC world news dataset. The data consists of a total of 2225 text documents from the BBC world news website corresponding to the stories covered in 2004-2005. The total size of the data is around 5 MB. The data is pre-classified into 5 different class labels.
6 days ago WEB The traditional text classification methods are based on machine learning. It requires a large amount of artificially labeled training data as well as human participation. However, it is common that ignoring the contextual information and the word order information in such a way, and often exist some problems such as data sparseness and latitudinal explosion. …
1 week ago WEB Key takeaway: 'This paper demonstrates text classification and summarization using machine learning techniques and algorithms, transforming natural language documents into uniform structures for training and displaying summarized reports to the user.' ... Text classification of BBC news articles and text summarization using text rank. Abhishek ...
6 days ago WEB dataset/dataset.csv: csv file containing "news" and "type" as columns. "news" column represent news article and "type" represents news category among business, entertainment, politics, sport, tech. model/get_data.py: To gather all txt files into one csv file contianing two columns ("news","type"). After successfull execution it will create ...
4 days ago WEB attempts to select the same population of news stories can produce dramatically different outcomes. In our running example, using keyword searches produces a larger corpus than using pre-defined subject categories (developed by LexisNexis), with a higher pro-portion of relevant articles. Since keywords also offer the advantage of transparency over
1 week ago WEB Authorship classification can be useful for plagiarism or detecting fake accounts and topic classification can be helpful for sorting or searching a dataset. The 2017 Vox Media is an understudied dataset that has advantages over other contemporary news article datasets in terms of the number of articles as well as labeled topics and authors.
1 week ago WEB Text mining has gained considerable importance in recent years, especially in the last few. Users can now get information from many different places, such as digital media, print media, electronic media, and others. Text categorization is the method by which researchers organize the huge quantities of unstructured data generated as a result of …
1 week ago WEB BBC News Text Classification - Medium. 4 days ago The text needs to be transformed to vectors so as the algorithms will be able make predictions. In this case it will be used the Term Frequency — Inverse Document Frequency (TFIDF) weight to evaluate how important a word is to a document in a collection of documents.
2 days ago WEB Feb 15, 2024 · Tokenization: Tokenization is the process of breaking down text into smaller chunks, like words or phrases, known as tokens. These tokens serve as the basic building blocks for building up our NLP model. Stemming and Lemmatization: These are techniques used to reduce words to their base or root forms. Stemming removes prefixes and …
1 week ago WEB The application domain is news articles written in English that belong to four categories: Business-Finance, Lifestyle-Leisure, Science-Technology and Sports downloaded from three well-known news web-sites (BBC, Reuters, and TheGuardian). Various classification experiments have been performed with the Random Forests machine …
1 day ago WEB Visit BBC News for up-to-the-minute news, breaking news, video, audio and feature stories. BBC News provides trusted World and UK news as well as local and regional perspectives. Also ...