Sarcasm detection in Tamil-English code-mixed text presents a unique challenge, particularly when traditional machine learning models are employed. This paper explores the application of conventional algorithms such as random forest, logistic regression, and naive Bayes, as well as the transformer-based BERT model. Performance evaluation uses four datasets, focusing on key metrics such as accuracy, precision, recall, and F1-score. BERT demonstrates superior performance, efectively capturing contextual nuances in sarcasm detection, making it a more viable approach for multilingual and code-mixed environments. Future work may expand on these ifndings by utilizing advanced transformer architectures and incorporating additional features like sentiment and emoji-based cues.
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Hybrid models, such as those developed by Jain et al. [
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Multimodal approaches have further advanced the field. Poria et al. [
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This section describes the approach taken to identify sarcasm in code-mixed Tamil-English text by utilising the transformer-based BERT model in addition to conventional machine learning models. Data preprocessing, model implementation, and evaluation are some of the crucial steps in the process.
The dataset for sarcasm detection is composed of multiple files, each serving a specific purpose in the analysis process. The first dataset, ‘Change Makers Tamil.csv‘, contains 47,960 rows and two columns: ‘Id‘ and ‘Labels‘. The ‘Labels‘ column identifies whether the content is sarcastic or non-sarcastic. This dataset provides labels necessary for training models on identifying sarcasm.
The second dataset, ‘sarcasm tam dev.csv‘, includes text data in a multilingual format (Tamil and English), with two columns: ‘Text‘ and ‘Labels‘. The ‘Text‘ column contains the actual social media content collected from platforms like Facebook and Twitter, and the ‘Labels‘ column classifies each text as either sarcastic or non-sarcastic.
The third dataset, ‘sarcasm tam test without labels.csv‘, contains only the ‘ID‘ and ‘Text‘ columns, representing the test set where labels are not provided. This dataset will be used for evaluating the model’s ability to predict sarcasm.
The fourth dataset, ’sarcasm tam train.csv’, comprises both ’Text’ and ’Labels’ columns , serving as the training data for the model. This dataset will be utilized to train the model to diferentiate between sarcastic and non-sarcastic text, ensuring it learns the patterns for sarcasm detection in the Tamil language.
In preprocessing, several steps were applied to clean and prepare the text for model training. Tokenization was performed while retaining both Tamil and English tokens. Text was converted to lowercase, and transliterated Tamil words were normalized. Special characters, digits, and punctuation were removed to reduce noise in the data. Additionally, stopwords from both Tamil and English were ifltered out to focus on meaningful content.
Sequence padding was applied to ensure uniform input lengths for the models. Contextual embeddings were generated using BERT to capture nuanced meaning from the text, while more traditional models utilized GloVe and Word2Vec embeddings. Finally, the dataset was split into training and testing sets (80-20 split), with cross-validation applied to ensure robust model evaluation. This preprocessing pipeline ensured the data was clean, consistent, and ready for sarcasm detection models.
BERT (Bidirectional Encoder Representations from Transformers) can interpret context and pick up on little linguistic clues, it is essential for sarcasm detection. In contrast to conventional models, BERT makes use of a transformer-based architecture with a bidirectional attention mechanism, which allows it to take into account the words that come before and after one another in a phrase at the same time. This bi-directional feature is essential for sarcasm recognition, particularly in code-mixed Tamil-English language where sarcastic statements must be understood in context.
Since sarcasm frequently relies on opposing literal and intended interpretations, BERT’s pre-trained embeddings capture deep contextual links between words, which makes it particularly successful in identifying sarcasm. By honing BERT’s performance on the sarcasm detection task, one can help it better distinguish between sarcastic and non-sarcastic comments by teaching it task-specific subtleties in code-mixed text. In comparison to standard models, the BERT-based model outperforms them in capturing the complexity of multilingual sarcasm, as evidenced by its better accuracy and F1-score. Performance measurements show that BERT performs significantly better than models such as Random Forest and Naive Bayes. The BERT classification report is shown in Table 1.
Beyond its eficiency, BERT’s adaptability makes it scalable and suitable for real-world applications since it can be optimised for a variety of natural language processing jobs. BERT is a recommended option for text classification tasks like sarcasm detection because of its strong design and capacity to handle large-scale datasets.
Formula : In the BERT architecture, parameters are the quantity of attention heads, hidden units, and layers. BERT is incredibly successful in sarcasm recognition because the embedding layer uses token, positional, and segment embeddings to capture word associations and attention layers model contextual dependencies.
By aggregating the predictions of several decision trees, Random Forest is an ensemble learning technique that is important for sarcasm identification. To increase generalisation and decrease overfitting, each tree in the forest is trained using a diferent random subset of the features and data. As they combine the results from diferent trees to produce more reliable and precise predictions, Random Forests do exceptionally well in complicated classification tasks.
Random Forest uses its capacity to identify patterns in the data to detect sarcasm by taking into account several word attributes including frequency and word pairings (n-grams). Despite its reliance on surface-level features such as TF-IDF vectors and bag-of-words, Random Forest is a strong foundation for sarcasm detection, especially in dificult code-mixed Tamil-English language, since it can handle the non-linear correlations between these features.
The Random Forest model struggles to capture the deeper contextual details that are essential for sarcasm detection, which makes it less accurate than deep learning techniques like BERT. Nevertheless, it still achieves a respectable level of accuracy. However, it is a useful tool in many text classification jobs due to its ease of use, interpretability, and capacity to handle big datasets with little adjustment.The Random Forest model’s classification report is displayed in Table 2.
Formula : The number of trees in the forest and the depth of each tree determine how many parameters there are in Random Forest. The model can achieve better generalisation than a single decision tree since each decision tree is constructed using a portion of the data and characteristics, and the final result is the majority vote or average of all tree forecasts.
Logistic Regression is a popular machine learning model,because of its interpretability and efectiveness in binary classification problems, for sarcasm detection. It is straightforward yet efective. It works by estimating the likelihood that an input, given its attributes and the goal label, would fall into a particular class—in this example, sarcastic or non-sarcastic. The output probabilities of logistic regression are subjected to a sigmoid function before being thresholded to provide binary predictions.
Word frequencies, n-grams, and TF-IDF vectors are some of the variables that Logistic Regression utilises to categorise text in order to detect sarcasm in Tamil-English code-mixed text. The deeper contextual and non-linear linkages that are frequently essential for sarcasm identification can be dificult for Logistic Regression to capture, even though it is excellent at handling linearly separable data. This is especially true in multilingual literature where meaning is dependent on nuanced linguistic cues.
Because of its simplicity and eficiency, Logistic Regression performs fairly well as a baseline model despite its limits in addressing complicated patterns; it achieves moderate accuracy in many classification tasks. It is especially helpful for short assessments and in situations where processing power is scarce.The classification report for logistic regression is displayed in Table 3.
Formula : The weights assigned to each feature in logistic regression are the parameters, and they are discovered by gradient descent in order to minimise the log loss function. By reflecting each feature’s contribution to the classification, these weights enable the model to forecast based on the linear relationship between the target output and the input data.
Because of its efectiveness and simplicity, the probabilistic machine learning model Naive Bayes is frequently employed for sarcasm detection. The model is computationally eficient because it applies Bayes’ Theorem under the assumption that all characteristics (words) are independent, which is rarely the case in real-world data. By multiplying the conditional probabilities of each word in the text with a class label, Naive Bayes determines if a text is sardonic or not. For text classification tasks such as sarcasm detection, it frequently achieves good results, despite the naive independence assumption.
Word frequencies and TF-IDF scores are two examples of features that Naive Bayes utilises to classify text in sarcasm detection for Tamil-English code-mixed text. It performs best when sarcasm is expressed using particular terms or patterns that can be statistically distinguished from non-sarcastic classes. However, compared to more sophisticated models like BERT, its independence assumption restricts its capacity to capture intricate contextual links, making it less successful for subtle sarcasm detection.
Naive Bayes is a good choice for first phase classification jobs since it is simple to use, computationally quick, and performs quite well on limited datasets or as a baseline model.The Naive Bayes classification report is displayed in Table 4.
The results reveal that the BERT model significantly outperforms traditional machine learning models Random Forest, Logistic Regression, and Naive Bayes in detecting sarcasm in Tamil English code mixed text. BERT achieved higher accuracy, precision, recall, and F1-score across four datasets, showcasing its ability to capture the nuanced context and cultural subtleties of sarcasm. In contrast, traditional models, which rely on surface level features, struggled to recognize these complexities. The success of BERT can be attributed to its transformer architecture, which employs bi-directional attention to analyze word relationships in context. This indicates that advanced models like BERT are more efective for sarcasm detection in multilingual environments, and future eforts should consider incorporating features like sentiment analysis and emoji recognition for further improvements.
In conclusion, the findings highlight the efectiveness of BERT for sarcasm detection in Tamil-English code-mixed text, demonstrating its superiority over traditional machine learning algorithms. The results emphasize the need for advanced models that can understand contextual and cultural nuances. Future research should explore additional transformer architectures and incorporate features such as sentiment analysis to enhance detection accuracy even further. The insights gained can significantly improve sarcasm detection systems in multilingual contexts.
Future research on sarcasm detection should explore advanced transformer architectures beyond BERT, such as RoBERTa and GPT, to enhance context understanding. Incorporating multimodal data, including audio and visual cues, can improve detection accuracy by capturing tone and facial expressions. Additionally, expanding datasets to include diverse languages and dialects will enhance model generalization. Integrating sentiment analysis and emoji recognition can refine sarcasm detection further. Finally, developing real-time systems for social media monitoring and conversational agents could translate research findings into practical applications, improving understanding of human communication.
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