discourse-analysis

To identify framing and power relations in political discourse, you can apply critical discourse analysis frameworks like Fairclough's 3D model to extract problem definitions, causal blame, and legitimation strategies from text.

Analyzing narrative structure and legitimation strategies in qualitative research involves detecting moral evaluations and justification patterns using NLP pipelines that extract stance markers and agency cues from social science text.

Yes, you can use computational text analysis for critical discourse analysis on Chinese political materials by detecting institutional framing markers and term-driven discourse features specific to Chinese-language text.

Extracting stance markers and intertextuality signals in media analysis requires running an NLP pipeline that detects certainty versus hedging patterns and quotation cues to evaluate rhetoric and framing rates.

Discourse analysis works with pandas for corpus-assisted study by using lightweight computational indicators to analyze text-plus-image coherence and track how collective meanings are constructed over time.

A limitation of using lightweight computational indicators for critical discourse analysis is that they rely on regex and pandas rather than deep machine learning, which may restrict complex multimodal coherence evaluation across large corpora.