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In recent years, life insurance has increased its share of the Colombian financial market. To strengthen this positioning, insurers have the challenge of reinventing their products, adjusting them to the demands of their clients and thus increasing sales. Thus, with the aim of integrally encouraging the development of life insurance and dynamize its offer in the country, in this master's thesis we design, develop and evaluate a model of natural language processing trained in the profiling task, based on unstructured data from Twitter. With this data, a segmentation of users is generated by evaluating dietary habits, physical activity and sentiment of publications, allowing to offer a preferential rate for the identified fragments with lower risk that generates a stimulus in the sale and creates economic value for insurers. The model is validated by relating the results to the mortality rate at the departmental level, obtaining a moderate Pearson correlation of 0.56 for the urban departments of Colombia.