Physiological Perspectives on Women Quitting Jobs

The complex subject of why women leave their employment has been investigated from a variety of perspectives, including social, economic, and psychological aspects. One of the most important, though occasionally overlooked, perspectives is the physiological one. By applying natural language processing NLP, polarity, and sentiment analysis to women's utterances about leaving their jobs, this research seeks to understand the physiological theories that underlie this behaviour. According to the specific example presented, one important aspect that contributes to this attitude is the thought of being passed up for leadership positions despite qualifications and dedication. As part of the data tokenization process, which is crucial to natural language processing, text is broken up into tokens, such as words or phrases. By calculating the frequency of each token after the text has been tokenized, you may identify the text's common themes or subjects. A further NLP technique known as sentiment analysis examines the text's underlying emotions and may beused to assess social media postings, client reviews, and other sorts of text data. Another essential element of sentiment analysis is polarity, which refers to the level of positive or negative sentiment in a text and may be represented as a polarity score provided by sentiment analysis algorithms. Overall, these techniques help computers understand the form and content of text data, allowing for more effective analysis. Positive sentiment analysis spans from 0.25 to 0.75, neutral opinion is represented by 0, and the negative sentiment analysis extends from -0.75 to - 0.25. The range of the emotion scores is from -1 to 1.