The Importance of Document Classification

Oct 13, 2025 by pinkandtrash.com 24 views

Introduction

The rapid growth and dissemination of information in online documents have led to a significant increase in the amount of data available. To manage this abundant information, a system is needed that can organize text documents automatically, one of which is the classification of documents. Document classification is a process of grouping documents based on its categories, and one technique that is widely used in document classification is the Naïve Bayes Classifier (NBC). NBC is known for its simplicity, speed, and high accuracy.

Background

The development of information technology has triggered the rate of growth and dissemination of information in online documents with a very large amount. To manage this abundant information, a system is needed that can organize text documents automatically, one of which is the classification of documents. Document classification is a process of grouping documents based on its categories. One technique that is widely used in document classification is Naive Bayes Classifier (NBC). NBC is known for its simplicity, speed, and high accuracy.

The Naïve Bayes Classifier (NBC)

The Naïve Bayes Classifier (NBC) is a type of supervised learning algorithm that is widely used in document classification. It is based on Bayes' theorem, which is a mathematical formula that describes the probability of an event occurring. The NBC algorithm works by assuming that the features of a document are independent of each other, which is known as the "naïve" assumption. This assumption allows the algorithm to simplify the calculation of the probability of a document belonging to a particular category.

The Importance of Document Classification

Document classification is an important task in natural language processing (NLP) and information retrieval (IR). It is used in a variety of applications, including text classification, sentiment analysis, and topic modeling. Document classification is also used in many industries, such as finance, healthcare, and marketing, to classify documents into different categories based on their content.

The Use of Sub Parent Category and Parent Category

In this study, we analyzed the effect of the application of sub parent category and parent category on the accuracy of the classification of documents using the Naïve Bayes algorithm. In general, the classification of documents uses specific categories. However, in this study, these categories are grouped again into a more general category with the same domain, namely Sub Parent Category and Parent Category.

Theoretical Background

The use of sub parent category and parent category is expected to increase classification accuracy. This is because the many words that appear in documents that have similar characteristics between sub parent category and parent category. The sub parent category and parent category are used to group the categories into a more general category, which can help to reduce the number of categories and improve the accuracy of the classification.

Methodology

The methodology used in this study is based on the Naïve Bayes algorithm. The algorithm is trained on a dataset of documents that are classified into different categories. The dataset is then used to test the accuracy of the algorithm. The accuracy of the algorithm is measured by comparing the predicted categories with the actual categories.

Results

The results of this study showed that the use of sub parent category and parent category in the naïve bayes algorithm can indeed increase the accuracy of document classification. Classification accuracy reached 34.37% using sub parent category and parent category, higher than the accuracy of classification without using it which only reached 31.25%. This increased accuracy proves that the use of sub parent category and parent category can help reduce classification errors between categories. This is caused by its ability to identify words that have similar characteristics and group them more precisely.

Discussion

The results of this study make an important contribution in the development of a more accurate and effective document classification system. The application of sub parent category and parent category in the Naïve Bayes algorithm can be a solution to overcome the problem of the classification of documents with the same domain, which is often faced with difficulties in identifying differences in the characteristics of words between categories.

Conclusion

In conclusion, this study analyzed the effect of the application of sub parent category and parent category on the accuracy of the classification of documents using the Naïve Bayes algorithm. The results showed that the use of sub parent category and parent category can indeed increase the accuracy of document classification. This study contributes to the development of a more accurate and effective document classification system.

Recommendations

Based on the results of this study, we recommend the use of sub parent category and parent category in the Naïve Bayes algorithm for document classification. This can help to improve the accuracy of the classification and reduce classification errors between categories.

Limitations

This study has some limitations. The dataset used in this study is limited to a specific domain, and the results may not be generalizable to other domains. Additionally, the study only analyzed the effect of sub parent category and parent category on the accuracy of the classification, and did not investigate other factors that may affect the accuracy of the classification.

Future Research Directions

Future research directions include investigating the effect of other factors on the accuracy of the classification, such as the use of different algorithms or the use of different features. Additionally, future research can investigate the use of sub parent category and parent category in other domains, such as finance or healthcare.

References

[1] A. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," in Learning for Text Categorization, 1998.
[2] T. Joachims, "Text categorization with support vector machines: Learning with many relevant features," in Proceedings of the 10th European Conference on Machine Learning, 1998.
[3] Y. Yang and J. O. Pedersen, "A comparative study on feature selection in text categorization," in Proceedings of the 14th International Conference on Machine Learning, 1997.
Frequently Asked Questions (FAQs) about the Analysis of the Accuracy of the Naïve Bayes Algorithm in the Categorized Document Classification ====================================================================================================================================

Q: What is the Naïve Bayes algorithm?

A: The Naïve Bayes algorithm is a type of supervised learning algorithm that is widely used in document classification. It is based on Bayes' theorem, which is a mathematical formula that describes the probability of an event occurring.

Q: What is document classification?

A: Document classification is a process of grouping documents based on its categories. It is an important task in natural language processing (NLP) and information retrieval (IR).

Q: What is the purpose of this study?

A: The purpose of this study is to analyze the effect of the application of sub parent category and parent category on the accuracy of the classification of documents using the Naïve Bayes algorithm.

Q: What are sub parent category and parent category?

A: Sub parent category and parent category are more general categories that are used to group the specific categories into a more general category with the same domain.

Q: How does the use of sub parent category and parent category affect the accuracy of the classification?

A: The use of sub parent category and parent category can indeed increase the accuracy of document classification. Classification accuracy reached 34.37% using sub parent category and parent category, higher than the accuracy of classification without using it which only reached 31.25%.

Q: What are the limitations of this study?

A: This study has some limitations. The dataset used in this study is limited to a specific domain, and the results may not be generalizable to other domains. Additionally, the study only analyzed the effect of sub parent category and parent category on the accuracy of the classification, and did not investigate other factors that may affect the accuracy of the classification.

Q: What are the future research directions?

A: Future research directions include investigating the effect of other factors on the accuracy of the classification, such as the use of different algorithms or the use of different features. Additionally, future research can investigate the use of sub parent category and parent category in other domains, such as finance or healthcare.

Q: What are the implications of this study?

A: The results of this study make an important contribution in the development of a more accurate and effective document classification system. The application of sub parent category and parent category in the Naïve Bayes algorithm can be a solution to overcome the problem of the classification of documents with the same domain, which is often faced with difficulties in identifying differences in the characteristics of words between categories.

Q: What are the potential applications of this study?

A: The potential applications of this study include the development of more accurate and effective document classification systems, which can be used in a variety of industries, such as finance, healthcare, and marketing.

Q: What are the potential benefits of this study?

A: The potential benefits of this study include the improvement of the accuracy of document classification, which can lead to better decision-making and more effective communication.

Q: What are the potential challenges of this study?

A: The potential challenges of this study include the development of more complex and nuanced document classification systems, which can be more difficult to implement and evaluate.

Q: What are the potential future developments of this study?

A: The potential future developments of this study include the investigation of the use of sub parent category and parent category in other domains, such as finance or healthcare, and the development of more accurate and effective document classification systems.

Q: What are the potential future applications of this study?

A: The potential future applications of this study include the development of more accurate and effective document classification systems, which can be used in a variety of industries, such as finance, healthcare, and marketing.

Q: What are the potential future benefits of this study?

A: The potential future benefits of this study include the improvement of the accuracy of document classification, which can lead to better decision-making and more effective communication.

Q: What are the potential future challenges of this study?

A: The potential future challenges of this study include the development of more complex and nuanced document classification systems, which can be more difficult to implement and evaluate.