Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data
Bibliographic record
Abstract
Tweets are difficult to classify due to their simplicity and frequent use of non-standard orthodoxy or slang words. Although several studies have identified highly accurate sentiment data classifications, most have not been tested on Twitter data. Previous research on sentiment interpretation focused on binary or ternary sentiments in monolingual texts. However, emotions emerge in bilingual and multilingual texts. The emotions expressed in today's social media, including microblogs, are different. We use a dataset that combines everyday dialogue, easy and emotional stimulation to carry out the algorithm to create a balanced dataset with five labels: joy, sad, anger, fear, and neutral. This entails the preparation of datasets and conventional machine learning models. We categorized tweets using the Bidirectional Encoder Representations from Transformers (BERT) language model but are pre-trained in plain text instead of tweets using BERT Transfer Learning (TensorFlow Keras). In this paper we use the HuggingFace’s transformers library to fine-tune pretrained BERT model for a classification task which is termed as modified (M-BERT). Our modified (M-BERT) model is an average F1-score of 97.63% in all of our taxonomy, which leaves more space for change, is our modified (M-BERT) model. We show that the dual use of an F1-score as a combination of M-BERT and Machine Learning methods increases classification accuracy by 24.92%. as related to baseline BERT model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".