Authors : Dr. Vishal Tiwari, Rahel Gonjari, Ankur Singh, Janhvi Badwaik, Chaitanya Nawghare
DOI : 10.46335/IJIES.2024.9.7.11
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Abstract— The increasing importance of sentiment analysis has prompted the exploration of automated methods for tracking public perceptions of several entities. Traditionally, manual efforts were employed, but the digital age has enabled the automatic detection of sentiments from online news sources and social media platforms. This shift has motivated the entities to adopt fine-grained sentiment analysis to accurately gauge public sentiment. However, the diverse and often implicit nature of opinions in news articles presents a challenge for sentiment analysis. This study aims to address this challenge by applying a method for fine-grained sentiment analysis of news articles. The research design involves classifying sentences as positive, negative, or neutral in the context of news articles. The findings of this study have implications for marketing, public policy, and the understanding of public sentiment towards various entities.
