Classification of Nordic Political Tweets: Application of NLP and Machine Learning in Social Media Analysis

This article introduces an open-source project that uses natural language processing (NLP) and machine learning techniques to classify and analyze over 500,000 Nordic political tweets. It covers multiple stages including data collection, preprocessing, model training, topic modeling, and visualization, providing a complete analytical framework for research on political discourse in social media. The project is maintained by SamTheOneee1, and the code is open-sourced on GitHub.

In recent years, social media has become an important platform for political discussions and public discourse. Platforms like Twitter generate massive amounts of user-generated content every day, containing rich information on political opinions, emotional tendencies, and social dynamics. The Nordic region has a high level of digitalization and prominent political participation of citizens on social media, but systematic analysis faces challenges due to multilingualism and complex political contexts. This project builds a complete NLP pipeline to classify and perform sentiment analysis on over 500,000 Nordic political tweets.

The core dataset contains over 500,000 Nordic political tweets, sourced from real-time scraping via the Twitter API and public datasets on Kaggle. Each tweet includes metadata such as the posting user, timestamp, and interaction metrics (likes, retweets, reply counts). Covering multiple Nordic countries and languages, it provides a foundation for training robust models while also placing higher demands on preprocessing and feature extraction.

The project is developed in Python, with core dependencies including Pandas (data cleaning), NumPy (numerical computation), Scikit-learn (ML algorithms), NLTK (basic NLP), Gensim (topic modeling), and Matplotlib & Seaborn (visualization). The analysis workflow is: Data Acquisition → Cleaning → Text Preprocessing → Feature Engineering → Model Training → Topic Modeling → Result Visualization.

Text preprocessing targets social media features (URLs, special characters, mentions, hashtags, etc.), handles multilingual encoding, and customizes a stopword list for the political domain. Sentiment classification uses ML models to categorize emotions (positive/negative/neutral), combining semantic features and user metadata to improve accuracy. Topic modeling uses two algorithms: LDA (probabilistic generative model) and NMF (matrix factorization), which complement each other to reveal topic patterns.

The project provides rich visualization functions: sentiment distribution charts (showing emotional changes across different time periods/themes), topic word clouds (core keywords), time series charts (evolution of topic/emotion popularity), and confusion matrices (model performance evaluation). Results are stored in the results directory, and users can explore interactively via Jupyter Notebook.

The project has application value in multiple fields: political science research (election prediction, policy evaluation, polarization research); public opinion monitoring (government/public relations monitoring of public opinion trends); news communication analysis (tracking event propagation paths); and business intelligence (analyzing consumer feedback to guide marketing).

The project has room for improvement: For multilingual processing, specialized cross-language models can be introduced; pre-trained models like BERT can be tried to improve performance; expand to streaming processing to support real-time monitoring; and introduce causal inference methods to understand the causal relationships between variables.