Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model
Bibliographic record
Abstract
Abstract Banglish slang is a distinctive mashup of Bengali and English terms that is widely used in messaging applications, social media, and other casual contexts. It has grown to be an essential component of Bangladesh's linguistic landscape. The use of Banglish slang in social media comments has become more common among young people from Bangladesh in recent years. Yet, because traditional language processing methods are not appropriate for managing such linguistic complexity, automated text analysis systems find it difficult to handle this informal communication style. Our proposed model captures the unique components of Banglish slang, such as slang phrases, misspellings, and abbreviations, by combining character-level and word-level properties since it is the most common form of communication and improves user interaction and content moderation. We remove special characters from the dataset and transform all test results to lowercase. After then, the stop words are removed from the dataset. Furthermore, we do tokenization using a pad sequence prior to classification. Stack Bidirectional Long Short-Term Memory (Bi-LSTM) model, a neural network architecture used in Natural Language Processing (NLP), is a state-of-the-art model utilized in this research to classify slang in Banglish words. On the training set with an outstanding 99% accuracy and a strong 79.8% validation accuracy, the suggested model beats traditional approaches. The Mendeley dataset was used to analyze social media comments to detect instances of Banglish slang. To carry out the work, approximately five thousand comments are considered overall. The experimental findings show how well our recommended approach works to identify Banglish slang. This method can be useful in monitoring content and language learning, among other real-world applications.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".