Navigating Sentiment Analysis in Print and Social Media with Natural Language Processing (NLP)
Venkata Ramana Kaneti, T. Rajesh
sentiment analysis; emotion analysis; social media; affect computing
The global COVID-19 pandemic has ushered in a paradigm shift, compelling businesses to migrate their operations to the digital realm, resulting in a substantial upsurge in online shopping activities. However, the rapid rise of e-commerce has brought with it the conundrum of distinguishing reliable products and trustworthy sellers in an ever-expanding digital marketplace. To tackle this challenge, numerous online retailers have implemented comment sections and star ratings systems, aimed at assisting customers in making informed choices. Nevertheless, these customer ratings and comments often present ambiguity or misleading information. In response to this, machine learning models have emerged as a formidable tool for scrutinizing copious comments and reviews, offering more precise insights and recommendations. These models possess the capability to grasp the subtleties of human language and sentiments, enabling them to comprehensively analyze comments and reviews and ascertain the overall sentiment associated with a particular product or seller. This analytical process empowers customers with dependable and accurate information, facilitating their discernment of trustworthy products and sellers. Furthermore, these models offer a valuable avenue for businesses to refine their products and services, identifying areas for enhancement through customer feedback. Additionally, this approach can be seamlessly extended to evaluate headlines and news articles, providing a nuanced understanding of media sentiment. By analyzing various newspapers, it becomes possible to discern which publications are conveying negative, positive, or neutral sentiments to the public. This information is invaluable for policymakers and government officials, enabling them to gauge public sentiment on a range of issues and adapt their policies accordingly. Media organizations can similarly leverage this data to optimize their content and tailor their reporting to align with the preferences and perspectives of their audience.
Article Details
Unique Paper ID: 161702

Publication Volume & Issue: Volume 10, Issue 5

Page(s): 405 - 413
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