A term weighting method for identifying emotions from text content
Bibliographic record
Abstract
Since the inception of the concept of social networking, communication patterns have shifted drastically with the unmitigated trend in socializing over the Internet, especially when people began connecting via mobile devices. Nowadays people tend to use these modern communication systems to share their emotions with each other. Human emotions play a vital role in human relationships and people share their emotions through facial expressions, gestures, speech and text messages. However, text messaging is the most common and widely accepted method to exchange information among peers through the Internet and mobile networks. In comparison to other methods, identifying emotions from text messages is rather difficult for the recipient. Therefore, the need of automating the emotion recognition from textual content has increased. Utilization of text classification techniques can be considered as the most common approach of identifying emotions from textual content. Prior to applying a text classifier, the textual data should be transformed into a data structure that the classifier understands by conforming to a document representation model and term weighting method. For this research Vector Space Model (VSM) is used as the document representation model. This paper proposes an extension to the Term Frequency - Inverse Document Frequency (TF-IDF) weighting method to increase classification accuracy and explains experiments conducted to discover the best term weighting method in vector space to be used in feature (text term) extraction from Aman's emotion text corpus. The text classification is done using Oracle's ODM SVM tool and LibSVM tool.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".