Automated Detection and Analysis of Power Words in Persuasive Text Using Natural Language Processing
This work addresses the need for content creators, advertisers, and policymakers to enhance messaging effectiveness, though it appears incremental as it applies existing NLP methods to a specific domain.
The study tackled the problem of automatically detecting and analyzing power words in persuasive text by creating a custom lexicon and a Python package, The Text Monger, to identify their frequency and impact on sentiment and reader engagement across domains like marketing and politics.
Power words are terms that evoke strong emotional responses and significantly influence readers' behavior, playing a crucial role in fields like marketing, politics, and motivational writing. This study proposes a methodology for the automated detection and analysis of power words in persuasive text using a custom lexicon created from a comprehensive dataset scraped from online sources. A specialized Python package, The Text Monger, is created and employed to identify the presence and frequency of power words within a given text. By analyzing diverse datasets, including fictional excerpts, speeches, and marketing materials,the aim is to classify and assess the impact of power words on sentiment and reader engagement. The findings provide valuable insights into the effectiveness of power words across various domains, offering practical applications for content creators, advertisers, and policymakers looking to enhance their messaging and engagement strategies.