Abstract
Abstract: Tonality analysis using machine learning algorithms refers to the computational examination of written text with the objective of discerning and categorizing the emotional or subjective tone embedded within the content. In the realm of natural language processing (NLP), this analytical approach seeks to automate the process of understanding and classifying the sentiment expressed in textual data, facilitating a nuanced interpretation of language that extends beyond mere lexical meaning. At its core, the analysis of tonality involves the utilization of sophisticated machine learning models to distinguish and categorize the emotional underpinnings of text. This is particularly valuable in scenarios where vast amounts of unstructured textual data need to be processed, such as customer reviews, social media interactions, or news articles. The overarching goal is to automatically assign predefined tonal labels, such as positive, negative, or neutral, to segments of text, thereby enabling a more quantitative and systematic investigation of the subjective aspects of language. The process typically commences with the acquisition of raw textual data, which undergoes a series of preprocessing steps. These steps involve cleaning the text, breaking it into individual units (tokenization), and extracting relevant linguistic features. Feature extraction is a critical aspect, as it involves transforming the raw text into a format suitable for machine learning algorithms. This may encompass capturing word frequencies, utilizing n-grams to identify contextual patterns, and incorporating sentiment lexicon scores to gauge the emotions.
Published Version
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