social-analytics

You can aggregate cross-platform social data by processing performance CSVs from various platforms, which enables competitive benchmarking, share of voice calculations, and unified content performance reporting for marketing dashboards.

Transformer-based sentiment analysis applies deep learning models from the transformers library to classify emotional tone in social media text, providing automated sentiment scoring and crisis detection across aggregated cross-platform data.

Share of voice benchmarking measures your brand's market visibility relative to competitors across social platforms. You need it when evaluating competitive context, tracking market position, or informing cross-channel marketing strategy from aggregated social performance data.

Yes, this social analytics workflow leverages pandas for data manipulation and scikit-learn for model-based scoring, allowing you to process social performance CSVs and generate content performance insights for reporting dashboards.

The best way to calculate cross-platform share of voice is to aggregate performance CSVs from all social platforms into a unified dataset, then apply deterministic scripts to benchmark your brand's visibility against competitors.

Yes, you need social performance CSVs formatted as social_performance_<platform>.csv files. These upstream data-extraction outputs serve as the required inputs for transformer-based sentiment analysis, competitive benchmarking, and share of voice calculations.