Sentiment Analysis of Emotional Intensity as a Continuous Driver of Engagement and Algorithmic Visibility

Abstrak

This study investigates how emotional intensity, rather than sentiment direction, shapes engagement and algorithmic visibility in digital political discourse. Using sentiment analysis, a dataset of about 15,000 posts from Twitter (X) and YouTube was collected over a 30-day period and scored with a hybrid TextBlob, VADER, and BERT pipeline. Emotional strength (the absolute sentiment value) correlated moderately with engagement (r = 0.58, p < 0.05), whereas the directional sentiment score did not (r ≈ 0.05). Emotionally intense posts attracted about 2.4 times more engagement than neutral posts, and positive posts were the most frequent (41%) while neutral posts drew the lowest mean engagement. These results indicate that engagement-based ranking amplifies emotional magnitude over neutral or analytical content, which can narrow the diversity of visible expression. The findings give platform designers and policymakers a reproducible basis for assessing how affective dynamics shape visibility in algorithmically mediated public discourse.

Identifier

• OpenAlex: https://openalex.org/W7164572163
• DOI: https://doi.org/10.47776/nuai.v2i1.2006