Improving the accuracy of NMF

Oct 13, 2025 by pynkocean.com 70 views

Introduction

The rapid development of information and communication technology has led to an exponential increase in the number of online news sites. With so many sources of information available, searching for news topics that are being hotly discussed becomes more complicated and time-consuming if done manually. To overcome this problem, technology is needed that can automatically extract and analyze news. In this study, we explore the use of non-negative matrix factorization (NMF) to extract online news portal topics.

Background

The increasing number of online news sites has created a need for technology that can automatically extract and analyze news. One of the technologies used in this study is web crawler, which is a program that functions to explore web pages and collect relevant documents and data. In the context of this research, web crawler is used to extract information from the online news portal, which is then used to search for topics trends automatically with the help of algorithms.

Methodology

The process carried out in this study consists of several stages, starting with text pre-processing. This stage includes several steps, including:

Text Pre-Processing

Case-Folding: Change all letters into lowercase letters for consistency.
Tokenizing: breaks the text into words or tokens.
Filtering: Eliminating words that are meaningless (stop words).
Stemming: return the word to its basic form.

Topic Trend Extraction

After the pre-processing stage is complete, the next step is topic trend extraction. In this process, the weighting of words is carried out using the Terquency and Non-Negative Matrix Factorization (NMF) method. NMF is an effective technique for identifying patterns in data, so that they can group news based on relevant topics.

Results

In this study, there are three types of news groups processed, namely national news, entertainment, and technology, with a total of 842 articles. The final result of this study is in the form of topics produced in accordance with each news group that can be visualized in graphics. This visualization is important because it helps readers to quickly understand the ongoing news trends.

Analysis and Relevance

Topic trend extraction using technology such as NMF can provide many benefits. First, this technique can save time and energy compared to manual search. In a fast-changing information age, the ability to get insight from news data quickly is a necessity.

In addition, the Machine Learning-based approach as used in this study has a higher level of accuracy in identifying trends compared to traditional methods. With the increasing volume of available information, the use of this technology is increasingly important to maintain the relevance of the news.

In addition, visualized results can be a tool for journalists, researchers, and general readers in understanding the context of news and trends that arise, and assist in better decision making. This study opens opportunities for further research in the field of text analysis and natural language processing, which can be developed for other applications in various sectors.

Conclusion

Thus, the application of web crawler and NMF method in extraction of online news portal topics is an innovative step that can increase our understanding of the dynamics of information that continues to grow. This study demonstrates the potential of NMF in extracting online news portal topics and provides a foundation for further research in this area.

Future Research Directions

This study opens opportunities for further research in the field of text analysis and natural language processing, which can be developed for other applications in various sectors. Some potential future research directions include:

Improving the accuracy of NMF: Further research can be conducted to improve the accuracy of NMF in extracting online news portal topics.
Applying NMF to other domains: NMF can be applied to other domains such as social media, blogs, and forums to extract relevant topics and trends.
Developing a user-friendly interface: A user-friendly interface can be developed to allow users to easily access and visualize the extracted topics and trends.

References

[1] Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788-791.
[2] Kim, J., & Park, H. (2008). Non-negative matrix factorization for text mining. Journal of Intelligent Information Systems, 31(2), 147-164.
[3] Zhang, Y., & Zhang, Y. (2011). Non-negative matrix factorization for topic modeling. Journal of Machine Learning Research, 12, 1-34.

Note: The references provided are a selection of relevant papers in the field of non-negative matrix factorization and text mining.

Q: What is non-negative matrix factorization (NMF)?

A: Non-negative matrix factorization (NMF) is a technique used to decompose a matrix into two non-negative matrices, where the product of the two matrices is the original matrix. In the context of this study, NMF is used to extract online news portal topics.

Q: How does NMF work?

A: NMF works by decomposing a matrix of word frequencies into two non-negative matrices, where the rows of the first matrix represent the topics and the columns of the second matrix represent the words. The product of the two matrices is the original matrix of word frequencies.

Q: What are the benefits of using NMF for extracting online news portal topics?

A: The benefits of using NMF for extracting online news portal topics include:

Improved accuracy: NMF can improve the accuracy of topic extraction by identifying the underlying topics and words that are most relevant to each topic.
Reduced dimensionality: NMF can reduce the dimensionality of the data by identifying the most important topics and words, making it easier to visualize and analyze the data.
Improved interpretability: NMF can improve the interpretability of the data by providing a clear and concise representation of the topics and words that are most relevant to each topic.

Q: What are the limitations of using NMF for extracting online news portal topics?

A: The limitations of using NMF for extracting online news portal topics include:

Computational complexity: NMF can be computationally expensive, especially for large datasets.
Overfitting: NMF can suffer from overfitting, especially when the number of topics is large.
Interpretability: NMF can be difficult to interpret, especially when the number of topics is large.

Q: How can NMF be used in other domains?

A: NMF can be used in other domains such as:

Social media: NMF can be used to extract topics and trends from social media data.
Blogs: NMF can be used to extract topics and trends from blog data.
Forums: NMF can be used to extract topics and trends from forum data.

Q: What are the future research directions for NMF?

A: The future research directions for NMF include:

Improving the accuracy of NMF: Further research can be conducted to improve the accuracy of NMF in extracting online news portal topics.
Applying NMF to other domains: NMF can be applied to other domains such as social media, blogs, and forums to extract relevant topics and trends.
Developing a user-friendly interface: A user-friendly interface can be developed to allow users to easily access and visualize the extracted topics and trends.

Q: What are the potential applications of NMF in real-world scenarios?

A: The potential applications of NMF in real-world scenarios include:

News recommendation systems: NMF can be used to recommend news articles to users based on their interests and preferences.
Social media monitoring: NMF can be used to monitor social media conversations and identify trends and topics.
Market research: NMF can be used to analyze market data and identify trends and topics.

Q: What are the potential challenges of implementing NMF in real-world scenarios?

A: The potential challenges of implementing NMF in real-world scenarios include:

Data quality: NMF requires high-quality data to produce accurate results.
Computational resources: NMF can be computationally expensive, especially for large datasets.
Interpretability: NMF can be difficult to interpret, especially when the number of topics is large.

Q: What are the potential benefits of using NMF in real-world scenarios?

A: The potential benefits of using NMF in real-world scenarios include:

Improved accuracy: NMF can improve the accuracy of topic extraction by identifying the underlying topics and words that are most relevant to each topic.
Reduced dimensionality: NMF can reduce the dimensionality of the data by identifying the most important topics and words, making it easier to visualize and analyze the data.
Improved interpretability: NMF can improve the interpretability of the data by providing a clear and concise representation of the topics and words that are most relevant to each topic.