Bibliometric Analysis of Rural Economic Development: Convergence between Sustainable Agriculture, Digital Technology, and Community Engagement for Village Self-Reliance
Bibliographic record
Abstract
Sentiment analysis, often known as opinion mining, determines how people feel about any subject. Sarcasm is the expression of irony or mocking through the use of derogatory language. These phrases change the polarity of a positive feeling into a bad one or the other way around. The focus of the proposed research is to implement a domain-oriented sarcasm detection model with a XAI approach to justify classification results. This strategy emphasizes on bringing value to domain-specific text using an explainable approach. The research referred to political domain content from Reddit platform to get insights in the area. It identifies sarcastic context from textual information present on social media. As a part of sentiment analysis and sarcasm detection natural language processing (NLP) plays important role. The suggested methodology is identifying sarcasm using weighted average approach of long short term memory (LSTM) and support vector classifier (SVC). implemented system has a fresh strategy to identify such sarcastic terms from sentences or forecast such sarcastic sentences. The suggested strategy used an Explainable Artificial Intelligence (XAI) approach to identify sarcasm in a specific political domain text. As a part of XAI, counterfactual explanation is implemented to identify the sarcastic words from given text which is given with more weightage by model while training the model. The system has generated 75.75% F1 score as result of weighted average approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".