MCWA-LSTM with SELU for Text-Based Emotion Classification
Bibliographic record
Abstract
Text-based emotion classification involves determining text to categorize emotions like sadness, anger, fear, happy, and so on.It employs Natural Processing Language (NLP) techniques for understanding sentiment and emotional tone behind words.This technique is widely employed in social media, customer feedback analysis, etc., However, accurately classifying emotions from text remains challenging because of sarcasm, ambiguity, and contextual nuances of human language leads to incorrect emotional responses.This research proposes Monotonic Chunk Wise Attention Long Short-term Memory with Scaled Exponential Linear Unit (MCWA-LSTM with SELU) for mulri-label text based emotion classification.In traditional LSTM, MCWA is incorporated to focus on relevant chunks of input sequentially which minimize noise from irrelevant parts and captures significant context effectively.LSTM capture long-term dependencies and contextual information which makes effective for emotion classification whereas SELU improves learning by managing self-normalizing properties that enhance training and model stability.Therefore, MCWA-LSTM with SELU achieves high accuracy of 98.66%, 98.32% on SemEval-2018 Task1-C, GoEmotion datasets for multi-class which is 37.46% and 27.12% higher compared to Universal Conceptual Cognitive Annotation-Graph Attention Network (UCCA-GAT).The proposed method obtains high f1-score of 98.21% for binary classification on TEL-NLP dataset which is 15.21% higher than existing Bidirectional Encoder Representations from Transformer (BERT) and Clipped Asymmetric Loss (ASL).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".