A NATURAL LANGUAGE PROCESSING APPROACH TO COMPARATIVE SENTIMENT AND TOPIC ANALYSIS OF ENGLISH NATIONAL ANTHEMS
Bibliographic record
Abstract
National anthems are powerful symbols that capture a country’s ideals, past events, and cultural identity; they are more than just songs. They are important in forming and reflecting a nation’s collective consciousness because they are ceremonial and patriotic manifestations. Using cutting-edge natural language processing (NLP) techniques, this study compares the English national anthems of the US, UK, and Canada in order to examine their lyrical content in greater detail. This study aims to identify and clarify the underlying emotional tones, thematic consistency, and important references in these anthems by utilizing a variety of natural language processing (NLP) techniques, such as sentiment analysis, keyword extraction, named entity recognition, and topic modeling. Every anthem offers a rich tapestry of language that reflects the historical and cultural narratives of its country, providing a distinctive lens through which to view pride and identity. Insights into how these anthems convey national sentiments and values can be gained by using sentiment analysis, which enables us to measure the emotional undertones present in the lyrics. By highlighting the most important words and ideas, Keyword Extraction illuminates recurrent themes and focal points that characterize the meaning of each anthem. By recognizing and classifying noteworthy allusions to individuals, locations, and institutions, Named Entity Recognition (NER) provides insight into the historical and cultural backgrounds that influence the anthems. In order to map out the main concepts and motifs of each anthem, Topic Modeling identifies the main themes and subjects that run throughout the lyrics. In addition to identifying and contrasting these linguistic traits, the study looks into how they all work together to represent pride and national identity. This study offers a thorough understanding of how English-speaking countries express their collective identities through their anthems by comprehending the subtle ways in which these songs articulate and uphold national values. The results contribute to a more comprehensive understanding of the function of national anthems in cultural and national representation by providing insightful information about the emotional and thematic aspects of national pride.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".