Multi-modal emotional analysis in customer relation management and enhancing communication through integrated affective computing
Bibliographic record
Abstract
An important part of customer relationship management (CRM) is being able to read emails for emotional cues; this helps with both communication and keeping customers satisfied. This study aims to improve email emotion identification by creating a system combining visual clues, aural signals, and textual information. To analyze text and emoji, the system uses advanced affective computing techniques such as Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa), Convolutional Neural Networks (CNN) for images, Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM) for video, and Cross-Modal BERT for audio. Together, they enable a wider variety of emotional signals to be extracted and understood from email content, yielding more insightful results than text-based analysis could on its own. By facilitating better two-way communication and customer satisfaction, the technology intends to improve CRM by providing actionable information that firms can use to personalize responses. This research delves into the possible uses of multi-modal emotional analysis across different businesses dealing with customers and builds a strong foundation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".