Multiclass Sentiment Analysis for Identifying Political Viewpoints

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Girma Yohannis Bade, Olga Kolesnikova, Jose Luis Oropeza, Grigori Sidorov
• Venue: arXiv: cs.CL
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.11049v1
• PDF: https://arxiv.org/pdf/2608.11049v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

Researchers developed two machine-learning models to analyze political viewpoints from social media posts. The XGBoost model achieved an F1-score of 0.2835, and the BERT-based model achieved an F1-score of 0.2806 in a labeled dataset. This study shows the complexity of analyzing sentiment in political discourse on social media.

What the Document Says

• The rapid growth of social media has led to a vast amount of political discourse.
• Sentiment Analysis (SA) is a key task in Natural Language Processing (NLP).
• SA helps understand attitudes and opinions in textual data, particularly political discourse.
• Researchers are investigating multiclass sentiment analysis of political viewpoints on social media.

Key Concepts Mentioned

• social media
• political discourse
• Sentiment Analysis
• Natural Language Processing
• multiclass sentiment analysis

Claims Made by the Document

• The researchers designed and evaluated two machine-learning approaches for multiclass sentiment analysis.
• The XGBoost model achieved an F1-score of 0.2835 in the test set.

Evidence Used

• The text discusses the creation of models using XGBoost and BERT.
• The text mentions the F1-scores for the models.

Methods / Approach

• Machine-learning approaches based on XGBoost and BERT
• Training and evaluation of models on a labeled dataset

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

The rapid growth of social media has created vast amounts of political discourse, which provides valuable opportunities to analyze public opinions and identify different political perspectives. Sentiment Analysis (SA) is a core task in Natural Language Processing (NLP) that allows the computational study of attitudes and opinions in textual data, and has become increasingly important for understanding political discourse. In this work, we investigate multiclass sentiment analysis of political view- points on social media, that is to automatically discriminate multiple sentiment classes over political issues and figures. To solve this task we design and evaluate two machine-learning approaches based on XGBoost and BERT. We train and evaluate the models on a labeled dataset of political social media posts using standard classification metrics. The experimental results show that the XGBoost model reaches an F1-score of 0.2835 and the BERT- based model reaches an F1-score of 0.2806 on the test set. These results demonstrate the challenge of classifying complex and contextualized political discourse sentiment and provide a baseline for future research in multiclass political sentiment analysis.