Leading mineral producing countries
In terms of volume, the most exploited commodities worldwide are iron ore, coal, potash, and copper. China, Indonesia, and India are the largest coal producing countries. China is also the third-largest producer of iron ore. Indeed, China is becoming the top mining country for many commodities, especially for the highly demanded rare earths, of which China produced70 percent of the global production in 2022. Additionally, China is the world’s leading gold mining country.
Major mining companies
The mining industry’s leading companies based on market capitalization are Anglo-Australian BHP and Rio Tinto, followed by the Anglo-Swiss company Glencore, and Vale from Brazil. As of March 2023, BHP had a market capitalization of nearly 159 billion U.S. dollars. Measured by revenue, the top company active in mining worldwide was Glencore, generating some 256 billion U.S. dollars in 2022. However, a large share of Glencore’s revenue comes from commodity trading. In 2023, four of the world's ten leading mining companies based on revenue were based in the UK, while three were based in China.
The impact of the COVID-19 pandemic on the mining industry
The COVID-19 pandemic had a noteworthy impact on the global mining industry, as with nearly every industry. Mining companies and mine employees faced mine closures in line with some countries' lockdown regulations, outbreaks of the virus at work sites, and other issues during 2020 in relation to the pandemic. Overall, a notable decrease in the mine production of many mineral commodities was observed in 2020 compared to 2019 and 2021.
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FAQs
Topic modeling is a type of statistical modeling that uses unsupervised Machine Learning to identify clusters or groups of similar words within a body of text. This text mining method uses semantic structures in text to understand unstructured data without predefined tags or training data.
What is an example of a topic model? ›
It scans or 'mines' text to detect frequently used words or phrases and groups them to provide a summary that best represents the information in the document. For example, if an article includes: 'soccer', 'score', 'goal', 'Manchester United', and 'Chelsea', the topic model is football.
How much data is needed for topic modelling? ›
For best results: You should use at least 1,000 documents in each topic modeling job. Each document should be at least 3 sentences long. If a document consists of mostly numeric data, you should remove it from the corpus.
What are the best topic modeling techniques? ›
Two popular topic modeling techniques are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Their objective to discover hidden semantic patterns portrayed by text data is the same, but how they achieve it is different.
What is topic detection in text mining? ›
Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text's topic or theme.
What is the purpose of topic model? ›
Topic modelling looks to combine topics into a single, understandable structure. It's about grouping topics into broader concepts that make sense for a particular business or issue.
What is a topic in a topic model? ›
In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.
What is the basic topic modeling? ›
Topic modeling is a type of statistical modeling used to identify topics or themes within a collection of documents. It involves automatically clustering words that tend to co-occur frequently across multiple documents, with the aim of identifying groups of words that represent distinct topics.
Is topic modelling unsupervised learning? ›
Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified.
Is topic modeling quantitative or qualitative? ›
Researchers may use topic modeling as a means to generate unbiased classifications and metrics of textual (qualitative) data. Textual data can be then measured and used in quantitative analysis, especially in hypothesis testing.
Some algorithms used for Topic Modeling tasks are Latent Dirichlet Allocation, Latent Semantic Analysis, Correlated Topic Modeling, and Probabilistic Latent Semantic Analysis.
What is the algorithm used in topic modeling? ›
One popular algorithm for topic modeling is Latent Dirichlet Allocation (LDA). For example, consider a large collection of news articles. Applying LDA may reveal topics like “politics,” “technology,” and “sports.” Each topic consists of a set of words with associated probabilities.
What does topic modeling do? ›
Topic modeling is an unsupervised machine learning technique that's capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents.
What is topic model also known as? ›
Topic models are also referred to as probabilistic topic models, which refers to statistical algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity.
What is the difference between clustering and topic modeling? ›
Clustering seeks to split documents into a certain number of groups based on a similarity metric. Topic modeling seeks to discover latent topics that describe the collection of documents. A topic represents a group of words that frequently occur together.
