Classification of Poverty Condition Using Natural Language Processing

Santa, Guberney Muñetón; Escobar-Grisales, Daniel; López-Pabón, Felipe Orlando; Pérez-Toro, Paula Andrea; Orozco-Arroyave, Juan Rafael

doi:10.1007/s11205-022-02883-z

Cited by 6 publications

(7 citation statements)

References 54 publications

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“…The word count excluded the words spoken by the interviewer. Further details about this corpus can be found in [33].…”

Section: Data Collection Processmentioning

confidence: 99%

“…NLP methods generate specific models that encode information about 'what the people say' and 'which concepts are more relevant to them.' According to our investigations [33], most of the available data comes from the interaction between Internet users. Existing language models predominantly reproduce particular points of view that are valid in the wealthiest parts of the world [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…This is primarily because the individuals delivering the discourse on the Internet or to the media are not poor, resulting in a language that does not correspond to that used by actual impoverished individuals. Marginalized people are underrepresented, and the resulting data are not representative enough to create accurate models [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Identifying dimensions and weighting for Poverty and Well-being Measurements through natural language of people.

Santa

Orozco-Arroyave

2023

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Purpose: Poverty and well-being indexes are composed of valuable aspects of life that are worth measuring, commonly referred to as dimensions. The objective of this study is to identify dimensions of poverty and well-being based on the natural language used by individuals. Additionally, we propose a methodology for extracting dimensional weights to assess indicators based on people’s concerns. Methods: Through the application of various topic modeling techniques within the Natural Language Processing approach, we have identified significant variables or dimensions related to poverty and well-being. We have implemented transfer learning using a zero-shot classification model to determine weights for the identified dimensions. Results: In the specific cases examined in this study, the most important dimensions of poverty are associated with: lack of opportunities, employment, lack of spirit, money, and progress; while in the context of well-being, the top four dimensions identified are: living well, basic needs, being nourished, and health. Conclusion: By using our semi-automatic methodology, which incorporates natural language processing, we offer a valuable contribution to the literature, enabling the identification of variables and the allocation of weights for constructing comprehensive social indicators. We recommend the use of topic modeling techniques for identifying dimensions in multidimensional wellbeing and poverty indicators, as they empower individuals by providing them with a platform to voice their concerns and ensuring their representation when designing and evaluating social indicators.

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“…The word count excluded the words spoken by the interviewer. Further details about this corpus can be found in [33].…”

Section: Data Collection Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying dimensions and weighting for Poverty and Well-being Measurements through natural language of people.

Santa

Orozco-Arroyave

2023

Preprint

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“…The contribution of this paper is that a new variable-feelings of people about poverty-was included in poverty analyses, which has not been performed before [7]. The research was conducted in Medellín (Colombia), with families participating in the social program "Medellín Solidaria: Familias Medellín", which is aimed at combating against extreme poverty.…”

Section: Classification Of Poverty Condition Using Natural Language P...mentioning

confidence: 99%

“…Poverty is a phenomenon that does not have one unique definition. There are various approaches in defining and measuring poverty, but in general, all of them can be divided into two large groups: monetary and non-monetary approaches [6,7]. The first and most widespread approach in poverty measurement is using a monetary approach: People are considered poor when they do not have enough money to maintain their livelihood [8].…”

Section: Introductionmentioning

confidence: 99%

Utilities of Artificial Intelligence in Poverty Prediction: A Review

et al. 2022

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Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

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Natural Language Processing System for Text Classification Corpus Based on Machine Learning

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in 5 levels. TFIDF TextRank text classification method based on key content extraction and text classification model based on CNN and BERT model were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

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Classification of Poverty Condition Using Natural Language Processing

Cited by 6 publications

References 54 publications

Identifying dimensions and weighting for Poverty and Well-being Measurements through natural language of people.

Identifying dimensions and weighting for Poverty and Well-being Measurements through natural language of people.

Utilities of Artificial Intelligence in Poverty Prediction: A Review

Natural Language Processing System for Text Classification Corpus Based on Machine Learning

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