The Sentiment Analysis on News Reporting: The Sentiment of Adjectives in News Articles
DOI:
https://doi.org/10.53840/ejbl.v5i2.154Keywords:
adjectives, corpus linguistics, emotive words, sentiment analysis, textual analysisAbstract
This research paper investigates the use of adjectives in online news reporting and their impact on reader engagement and the overall sentiment of the news articles. The study is grounded in the theoretical framework of corpus linguistics, exploring the lexical choices of adjectives in a diverse range of news articles from various genres. The analysis is based on a mini corpus of 889 news articles with a total of 618,796 tokens, and it employs Laurence Anthony's AntConc software for data analysis. The research findings reveal that adjectives play a crucial role in shaping the tone and sentiment of news articles. The study categorises adjectives into three groups: positive, negative, and neutral. Notably, the analysis demonstrates that while a significant portion of adjectives used in news articles is neutral and context-dependent, there is a balanced presence of both positive and negative adjectives. This suggests that news reporting aims for objectivity in the content of the articles. In conclusion, this research highlights the importance of adjectival phrases in news reporting, demonstrating that adjectives can influence reader emotions and affect the newsworthiness of articles. While negative adjectives may be more prevalent in headlines to capture attention, the content of news articles tends to maintain objectivity and balance. Future research could delve deeper into the differences between news headlines and article content to better understand the strategies employed by journalists in online news reporting.
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