MM325M5_Project_part4_PrithviK

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1 Gender Pay Gap: A Textual Analysis Prithvi Kothakonda MM325M5 Professor Whiffen March 14, 2024

2 The gender pay gap remains a persistent issue worldwide, reflecting systemic inequalities in the workforce. Despite advancements in gender equality, women, on average, still earn less than men in many professions and industries. This report aims to analyze recent articles from media sources to extract verbiage patterns related to the gender pay gap. The articles were sourced from reputable news outlets and analyzed using text mining techniques to uncover insights into societal perspectives, discussions, and potential solutions regarding this issue. The following articles I used all shared the same sentiment: Women get paid less than men. The three techniques I utilized are Word Frequency Analysis, Sentiment Analysis and Topic Modeling. Word Frequency Analysis involves counting the frequency of words appearing in the articles. Commonly used libraries in R, such as `tm` and `tidytext`, can be utilized for this analysis. The output reflects the most frequently mentioned terms related to the gender pay gap, providing insights into the key topics and themes discussed in the articles. Positive outcomes would include terms such as "equal pay," "gender parity," and "fair compensation," indicating a focus on addressing the issue. Negative outcomes might involve terms like "discrimination," "wage gap," and "inequality," highlighting the challenges and disparities present. Next, I used Sentiment Analysis which evaluates the sentiment or tone of the text, determining whether it is positive, negative, or neutral. This can be achieved using sentiment lexicons and machine learning algorithms. The output reveals the overall sentiment towards the gender pay gap discussion in the articles. Positive sentiment indicates optimism or progress, while negative sentiment suggests frustration or dissatisfaction. Positive outcomes would be reflected in articles with sentiments conveying hopefulness, progress, or advocacy for change. Conversely, negative outcomes might involve sentiments expressing anger, disillusionment, or resignation regarding the issue. Finally, I utilized Topic Modeling. Topic modeling identifies latent topics within a

3 corpus of text documents. Techniques such as Latent Dirichlet Allocation (LDA) can be applied to extract these topics. The output displays clusters of words representing different topics discussed in the articles. Each topic provides insight into specific aspects of the gender pay gap discourse. Positive outcomes involve topics centered around solutions, progress, and empowerment. Negative outcomes may include topics related to discrimination, barriers, and disparities. Text mining provides a powerful tool for analyzing large volumes of text data efficiently and extracting valuable insights. In the context of the gender pay gap, it enables organizations to understand prevailing attitudes, identify key issues, and track progress towards gender equality. Positive outcomes in the analysis indicate a proactive approach towards addressing inequalities, while negative outcomes highlight persistent challenges that require attention. Beyond gender pay gap analysis, text mining can be applied to various contexts, including sentiment analysis of customer feedback, topic modeling in academic research, and trend analysis in social media monitoring.

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4 References: Articles: Innes, M. (2024, February 6). Marketing’s gender pay gap narrows, but remains double the national average. Marketing Week . https://www.marketingweek.com/marketing-gender- pay-gap-narrows/ Observer, F., & Observer, F. (2023, December 13). Education does not resolve gender wage gap . Fullerton Observer. https://fullertonobserver.com/2023/12/13/education-does-not- resolve-gender-wage-gap/ Pew Research Center. (2023, March 1). Gender pay gap remained stable over past 20 years in US | Pew Research Center . https://www.pewresearch.org/short-reads/2023/03/01/gender- pay-gap-facts/ R Coding Resources: Mhatre, S. (2021, September 15). Text Mining and Sentiment Analysis: Analysis with R . Simple Talk. https://www.red-gate.com/simple-talk/databases/sql-server/bi-sql-server/text- mining-and-sentiment-analysis-with-r/ R: Analyze word frequencies . (n.d.). https://search.r-project.org/CRAN/refmans/koRpus/html/freq.analysis-methods.html Schweinberger, M. (2023, April 5). Topic Modeling with R . https://ladal.edu.au/topicmodels.html

5 Graphical Content:

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