Summarizer Precision Value on Tribunnews Gorontalo in the Implementation of Online Discourse Sentiment Analysis
DOI:
10.33395/sinkron.v8i4.14070Keywords:
Precision, summarizer, online discourse, sentiment analysis, BERTAbstract
This research investigates the precision of a summarization-based sentiment analysis framework applied to online discourses, specifically from Tribunnews Gorontalo. This study aims to develop and evaluate a sentiment analysis framework that accurately parses complex meanings and nuances in online discourse. The research process begins with summarizing the content using Python, followed by tokenization and sentiment analysis using the BERT model. The precision of the sentiment analysis was meticulously measured. Results indicate that the precision analysis demonstrates that the Python-implemented model achieved a 86% precision rate when applied to ten online discourses from Tribunnews Gorontalo. This research contributes significantly to understanding public sentiments in online content, offering deeper and more accurate insights.
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Copyright (c) 2024 Rahmat Taufik R.L Bau
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
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