Sarcasm detection in natural language processing is particularly challenging when dealing with code-mixed languages, often seen on social media platforms. Code mixing, where users mix multiple languages within a single statement, complicates the task for traditional NLP models. This study applies various machine learning techniques to detect sarcasm in code-mixed text. The dataset, comprising labeled sarcastic and non-sarcastic samples, is preprocessed using text normalization and TF-IDF vectorization. In this work, sarcasm in code-mixed text is detected using four models: Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The dataset is assessed using F1-score, recall, accuracy, and precision. The results highlight the advantages and disadvantages of each model for sarcasm recognition in multilingual settings. In contrast to the majority of earlier studies, which focus on monolingual data, this investigation delves into the little-studied field of code-mixed sarcasm detection. In addition, it draws attention to the dificulties in managing casual social media text and makes recommendations for enhanced deep learning methods in the future.
On social media, sarcasm is frequently employed to convey irony, humor, or criticism. However, because
the meaning of the words is frequently diferent from their literal meaning, it can be challenging to
identify sarcasm in texts [
Dealing with code-mixed languages makes the task considerably more dificult. When two or more
languages are used in the same speech or discourse, this is known as code-mixing [
The purpose of this project is to create and evaluate machine learning models for sarcasm detection
in text that is code-mixed between Tamil and English. The objective is to assess the efectiveness of
these models and address the dificulties associated with managing multilingual data. [
The significance of sarcasm in security and commercial applications has made it an essential component
of sentiment analysis and opinion mining. The study conducted by Swami et al. [
Shetty et al. [
In their study, Shanmugavadivel et al. [
Although real-time adaptability is limited, Orosoo et al. (2024) present a Federated Bi-LSTM Model
for code-mixed text analysis that outperforms conventional techniques and achieves 0.99% accuracy
[
We trained machine learning models to identify sarcasm in code-mixed social media chats using a dataset of 29,571 entries. A example of text marked as sarcastic or non-sarcastic is included with every entry. These samples include elements like emoticons, colloquial expressions, and informal spellings that make it dificult to identify sarcasm.
We used a diferent test dataset with 6,338 entries for evaluation. There are three diferent forms of
sarcasm in this dataset: implicit, explicit, and indirect. The models had to manage intricate language
and cultural subtleties in order to detect these types. The dataset additionally include slang, hashtags,
and acronyms to guarantee that it represents authentic code-mixed social media conversations [
TEXT CATEGORY Avara Control Pannunga Pls... Vera level Sarcastic expression... Thala...
Thenavattu movie ku appuram ippodhan Non-sarcastic jiiva anna mass acting panni irrukkangga movie blockbuster dhaan
Preparing and cleaning code-mixed data is essential, particularly for jobs involving natural language processing such as sarcasm identification in social media text [ 19]. Code-mixed content usually combines several languages, including Tamil and English, and frequently include informal aspects like slang, symbols, and typographical errors. This emphasizes how crucial it is to prepare and clean data thoroughly in order to guarantee eficient analysis and model performance.
Lowercasing: All text was converted to lowercase in order to create uniformity. This improves overall performance by preventing confusion brought on by varied letter cases and making it simpler for the model to match text.
Noise reduction: Extraneous elements such as special characters, hashtags, mentions, URLs, and punctuation were eliminated. This phase is crucial because it keeps the analysis concentrated on the text’s main elements, making it clearer and simpler for the model to comprehend.
Tokenization: Using a unique technique, we divided the text into smaller chunks, such as words or phrases. This helps the model capture the subtle variations in language use and is particularly helpful for assessing code-mixed material, which is text that contains various languages or dialects.
Handling Emojis and Emoticons: We turned emojis into text rather than eliminating them. Emojis frequently convey emotional meaning, which helps identify sarcasm. Maintaining these symbols aids the model’s comprehension of the text because, depending on the context, a smiley face may indicate sarcasm.
Extraction of Features: To identify key terms, we employed a technique known as TF-IDF [20]. In order for the model to concentrate on the most relevant terms that may indicate sarcasm or other significant meanings, this strategy assists in identifying words that are common in one text but rare in another.
For supervised learning, encode the target label (sarcastic or non-sarcastic). Sarcasm is usually binary, with 1 denoting sarcasm and 0 denoting non-sarcasm.
For binary classification tasks, logistic regression is a straightforward yet powerful classification model. TF-IDF characteristics are frequently combined with logistic regression in sarcasm detection to forecast sarcasm. Because TF-IDF efectively captures the significance of words in a text, it is useful for diferentiating between sarcastic and non-sarcastic information, which is why we utilized it in this work. Table 2 displays the logistic regression model’s performance metrics for sarcasm prediction.
As seen in Table 3, Support Vector Machines (SVMs) with linear kernels are well recognized for their dependable performance in text classification and their eficacy in managing high-dimensional feature spaces, including text data. Accuracy Precision
Recall F1-score 79.42 70 79 68 Metrics Accuracy Precision
Recall F1-score
Value
TF-IDF or n-gram features are frequently utilized with Naive Bayes, especially when dealing with sparse text data. Its probabilistic methodology aids in locating crucial textual expressions that suggest sarcasm. It successfully captures significant words that are essential for sarcasm recognition when paired with TF-IDF. When more than one language, such as Tamil and English, are utilized together, this concept works well. In text classification problems, Naive Bayes performs well even if it assumes feature independence. It uses word probabilities to identify sarcastic patterns. Because of this, it’s a good and eficient option for social media content analysis. For sarcasm detection, Naive Bayes works consistently, as Table 5 demonstrates. Accuracy Precision
Recall F1-score
This Figure 1 displays the procedures required to develop and assess a machine learning model for text data. Data preprocessing and loading are the first steps in the process. Stop words, unnecessary letters, and punctuation are among the noises that are eliminated from the raw data at this first stage. For uniformity, text normalization methods like stemming and lowercasing are also used. Inconsistencies are fixed and the data is prepared for analysis.
Converting the text to a numerical representation comes next after preprocessing. Textual input is transformed into numerical vectors that machine learning models may handle using methods like TF-IDF or word embeddings (e.g., Word2Vec, GloVe). In order to enhance model performance, feature extraction is also carried out to determine which words or phrases are most relevant to the prediction task.
The data is separated into training and test sets after transformation. The machine learning model is trained using the training set, and the test set is kept apart for assessment. To train the model, standard techniques like Support Vector Machines, Random Forests, Naive Bayes, and Logistic Regression can be used. The model learns to find patterns and connections between the input features and the target labels during training.
Predictions are made on the test set following training, and the model’s performance is assessed using a number of measures, including accuracy, precision, recall, and F1-score. The model’s ability to identify sarcasm or other desired results is indicated by these metrics. Should the performance be unsatisfactory, the model’s hyperparameters are adjusted by methods such as grid search or random search to improve the model’s precision and efectiveness.
The model can be used to generate predictions on fresh, unknown text data once it has been optimized. It could be necessary to regularly assess and retrain the model to make sure it keeps working well when fresh data is added.
Four machine learning models were used in this study to evaluate the ability to identify sarcasm in Tamil-English code-mixed text: Naive Bayes, Random Forest, Support Vector Classifier (SVC), and Logistic Regression. The most accurate of these was Logistic Regression, which had an accuracy of 79.42%. This demonstrates that when it comes to sarcasm detection in mixed-language social media content, Logistic Regression performs better than other models like Random Forest and SVC in terms of precision, recall, and F1-score.
According to the findings, it can be dificult to identify sarcasm in mixed-language texts due to language mixing and informal writing. Despite their efectiveness, the models failed to pick up on a few tiny sarcastic cues. To increase accuracy in future studies, deep learning may be used. Table 6 displays the models’ performance and shows that Logistic Regression was the most accurate and therefore more efective than the others.
Models Logistic Regression
Random Forest
Naive Bayes
SVM
Accuracy This study achieved an outstanding 79.42% accuracy rate using the Logistic Regression model. The classification report focuses on important performance indicators including precision, recall, and F1score to demonstrate how well the model separates sardonic from non-sarcastic text. Future research on code-mixed language will benefit from the well-structured and annotated dataset this study provides, which is a significant contribution to sentiment analysis. But there are still issues like overfitting and restrictions on using the model in certain situations. This work represents a significant breakthrough in sarcasm recognition despite these obstacles, providing a strong basis for future research and the creation of more sophisticated methods.
During the preparation of this work, the author(s) used ChatGPT in order to: drafting content, grammar and spelling check, etc. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content. C. Rajkumar, Overview of sarcasm identification of dravidian languages in dravidiancodemix@fire2024, in: Forum of Information Retrieval and Evaluation FIRE - 2024, DAIICT , Gandhinagar, 2024. [19] R. Kanakam, R. K. Nayak, Sarcasm detection on social networks using machine learning algorithms: A systematic review, in: 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, 2021, pp. 1130–1137. [20] R. Singh, R. Srivastava, A novel balancing technique with tf-idf matrix for short text classification to detect sarcasm, Int. J. Mech. Eng 7 (2022) 602–607.