Análisis de sentimientos en la red social Twitter mediante el procesamiento de lenguaje natural - Maldonado Ramones, Erik Stalyn (2022)
Organizations have begun to use sentiment mining, considering that it plays an important role in decision-making and market strategies. For their part, technologies evolve very quickly and natural language processing and machine learning contribute to this change, which today allows machines to understand the language used by human beings. Social networks have evolved in such a way that platforms are able to allow users to socialize, locate network members and form friends lists; one of the most important networks like Twitter allows you to send short opinion messages called tweets about any current event, managing to become an informative medium for society. For this reason, this research work is directed to the study of sentiment analysis in the social network Twitter that aims to discover the emotions that are hidden behind a piece of writing, which can be positive, negative or neutral. Sentiment analysis is a tool from which very valuable data can be extracted, for example for: an electoral campaign, an organization, or impact studies, etc. The data obtained from this sentiment analysis will allow us to understand the market, evaluate its trends, and even make financial predictions. In this way, the proposal turned out to present a sentiment analyzer that uses algorithms focused on defining opinions or attitudes using the TextBlob package in Python, for a polarity within a range between -1.0 and 1.0 that represents a negative or positive evaluation, and if equal to 0 a neutral evaluation. Useful for any search topic.