APPLICATION OF MACHINE LEARNING METHODS TO SOLVE THE NLP TEXT CLASSIFICATION PROBLEM BASED ON ANALYSIS OF SEMANTICS OF NATURAL LANGUAGE

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

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Using an Ensemble Learning Approach on Traditional Machine Learning Methods to Solve a Multi-Label Classification Problem

Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences - Algorithms for Intelligent Systems ◽

10.1007/978-981-15-7533-4_60 ◽

2021 ◽

pp. 761-772

Author(s):

Siddharth Basu ◽

Sanjay Kumar ◽

Sirjanpreet Singh Banga ◽

Harshit Garg

Keyword(s):

Machine Learning ◽

Ensemble Learning ◽

Classification Problem ◽

Learning Approach ◽

Learning Methods ◽

Machine Learning Methods

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Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches

10.4018/978-1-7998-8413-2.ch008 ◽

2022 ◽

pp. 171-195

Author(s):

Jale Bektaş

Keyword(s):

Machine Learning ◽

Text Mining ◽

Text Classification ◽

Integration Method ◽

Classification Problem ◽

Feature Representation ◽

Learning Approaches ◽

Machine Learning Methods ◽

Linguistic Approach ◽

Turkish Language

Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language.

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SPATIAL PLANNING TEXT INFORMATION PROCESSING WITH USE OF MACHINE LEARNING METHODS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-vi-4-w2-2020-95-2020 ◽

2020 ◽

Vol VI-4/W2-2020 ◽

pp. 95-102

Author(s):

I. Kaczmarek ◽

A. Iwaniak ◽

A. Świetlicka ◽

M. Piwowarczyk ◽

F. Harvey

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Development ◽

Classification Problem ◽

Learning Methods ◽

Machine Learning Methods ◽

Main Challenge ◽

Text Information ◽

The Moment ◽

Classification Tool

Abstract. Spatial development plans provide an important information on future land development capabilities. Unfortunately, at the moment access to planning information in Poland is limited. Despite many initiatives taken to standardize planning documents, the standard for recording plans has not yet been developed. Each of the planning areas has a symbol and a category of land use, which is different in each of the plans. For this reason, it is very difficult to carry out an analysis enabling aggregation of all areas with a specific, the same development function.The authors in the article conduct experiments aimed at using machine learning methods for the needs of processing the text part of plans and their classification. The main aim was to find the best method for grouping texts of zones with the same land use. The experiment consists in an attempt to automatically classify the texts of findings for individual areas into the 10 defined categories of land use. Thanks to this, it is possible to predict the future land use function for a specific zone text regulation and aggregate all zones with specific land use type.In the proposed solution for the classification problem of heterogeneous planning information authors used k-means algorithm and artificial neural networks. The main challenge for this solution, however, was not the design of the classification tool but rather the preprocessing of the text. In this paper an approach for text preprocessing as well as selected methods of text classification is presented. The results of the work indicate greater use of CNN's usability to solve the problem presented. K-means clustering produces clusters, in which texts are not grouped according to land use function, which is not useful in the context of zones aggregation.

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