Sarcasm, which is frequently characterized by irony or ridicule, is a complicated phenomena in language when the intended meaning difers from the literal utterance. Sarcasm detection in text poses major hurdles to natural language processing (NLP), especially in social media situations. In contrast to conventional sentiment analysis, sarcasm frequently hides its true meaning beneath colloquial language, making it challenging to identify without taking into account the context and minute changes in meaning.Using transliterated datasets, this study focuses on sarcasm recognition in Indic languages, particularly Tamil and Malayalam. We improve the text's accessibility for machine learning models by transliterating the native scripts into standard forms. The eficacy of four models in identifying sarcasm was tested: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). With an F1-score of 0.3710 and accuracy rates of 83.62% for Malayalam and 75.50% for Tamil, Logistic Regression was the model that performed the best overall.Our system demonstrated the resilience of our method by achieving a noteworthy rank 9 in a competitive sarcasm detection assignment.These ifndings highlight how crucial it is to combine customized machine learning models with transliteration in order to identify sarcasm in regional languages.
The practice of translating a word or phrase from one writing system to another while preserving the
original writing system’s sounds or letters is known as transliteration.When used skillfully,
transliterations and code-switching in languages such as Tamil and Malayalam can yield insightful linguistic
information that enhances the ability to recognize sarcasm [
Sarcasm recognition in Indian languages has been a major focus of research. The study [
Language nuances and code switching cause problems in Hindi, as they do in other Indian languages. The study discovered that collecting features from Indic scripts using models like SVM and Random Forest can improve sarcasm detection by leveraging language-specific characteristics.
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These researches emphasise the significance of dealing with linguistic diversity, exploiting multimodal inputs, and using advanced machine learning algorithms. Combining these approaches considerably increases model robustness and accuracy in identifying sarcasm in Indian languages.
The dataset used in this research features mixed-language utterances that include Tamil-English and Malayalam-English combinations, categorized as either sarcastic or non-sarcastic. The dataset is available at: https://codalab.lisn.upsaclay.fr/competitions/19310#participate. Text samples in Malayalam and Tamil that have been separated into training, validation, and test subsets make up the sarcasm recognition dataset. There are 21,740 non-sarcastic and 7,830 sarcastic text in the training set and 4,630 non-sarcastic and 1,706 sarcastic text in the validation set for the Tamil dataset. 6,338 text samples make up the test set, which will be used for label-free evaluation. There are 10,689 non-sarcastic and 2,499 sarcastic text in the training set and 2,305 non-sarcastic and 521 sarcastic text in the validation set of the Malayalam dataset. The model’s performance on unseen data will also be evaluated using the 2,826 unlabeled text samples in the test set. This dataset is set up to train and validate models for sarcasm detection in both languages.The dataset is an extensive collection for sarcasm identification in Tamil and Malayalam since it spans a wide range of situations and domains, from oficial writing to social media posts. The model’s capacity to generalize well to new data is improved by the quantity of the dataset, which guarantees that it is trained on a variety of samples. Furthermore, a diferent validation set makes sure that the models are adjusted to prevent overfitting, which strengthens their resilience.
A thorough preparation pipeline is crucial when working with both native-script and Romanized Tamil text data. The first step is transliterating Romanized Tamil into native Tamil script using the indic-transliteration module to ensure a consistent language representation during feature extraction.
Following transliteration, preprocessing is carried out to clean and standardize the data. All text is converted to lowercase to maintain uniformity, and punctuation, special characters, and non-alphabetical symbols are removed, as they don’t contribute to the text’s semantic meaning. Stopwords, common words like "and" ,"the" and "is" which don’t add significant meaning, are also removed using a specially compiled Tamil stopwords list, expanded from publicly available sources. This step reduces dimensionality and allows models to focus on informative words.
Tokenization is performed using the indic_tokenize module, designed to handle the complexities of Indian languages. The Tfidf Vectorizer then converts each sentence into meaningful tokens or words and transforms them into numerical features. By giving words that appear frequently within a document but rarely across the entire corpus higher weights, Tfidf Vectorizer captures the relative importance of each word. To maintain computational feasibility, the vocabulary is limited to the top 5,000 most frequent terms.
Four machine learning models are employed for sarcasm detection in Tamil text: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR). These models are selected for their ability to handle high-dimensional feature spaces and their eficiency in text classification tasks.
Initially, a bigger and more varied dataset was produced by combining the initial training and validation datasets. In order to improve the model’s ability to generalize, more data was made accessible for training. Following the datasets’ merger, an 80-20 split was implemented, whereby 20% of the merged data was set aside for testing and 80% was used for training. This method made it possible to train the model on a greater range of instances while still ofering a distinct test set for assessing how well it performed on data that wasn’t used for training.
For feature extraction, Tfidf Vectorizer was utilized to convert text into a numerical matrix, evaluating the significance of words in relation to the entire dataset. Word embeddings were also employed to translate words into continuous vector representations, capturing semantic relationships based on context. To preserve local context and better understand word associations, N-grams, particularly trigrams, were used. The selection of trigrams was aimed at incorporating patterns involving sequences of three words, which can provide a deeper understanding of context, especially for capturing nuanced expressions like sarcasm. The extracted features from these techniques were then used to train and test the machine learning models.
A popular classification technique for sarcasm detection, even in contexts with mixed language codes,
is logistic regression. The model uses a probability score between [
By categorizing texts based on their similarity to pre-labeled sarcastic or non-sarcastic samples, the k-Nearest Neighbors method (KNN) can detect sarcasm without needing complex frameworks. When applied to code-mixed languages, KNN analyzes features from both languages, such as word frequency, sentence structure, and contextual patterns, to find the closest matches. The final label is determined by the majority class of the nearest neighbors.
Although simple and easy to implement, KNN struggles to accurately identify sarcasm, especially in code-mixed texts. Traditional similarity metrics often fail to capture subtle cues like tone, wordplay, or the context of language switching, which are critical for detecting sarcasm. As a result, KNN’s efectiveness is limited in these scenarios. On the Tamil dataset, we achieved an accuracy of 73.51% with an F1 score of 0.238. For the Malayalam dataset, the accuracy reached 76.39%. This shows KNN’s limitations in identifying sarcasm in complex language settings.
When it comes to code-mixed languages like Tamil-English, Support Vector Machines (SVM) are especially helpful in diferentiating between messages that are sarcastic and those that are not. By converting the text into numerical features, such TF-IDF scores, and extracting linguistic traits unique to the code-mixed language, SVM finds the ideal hyperplane that divides the two groups. SVM is a good option since sarcasm in these kinds of writings frequently depends on how both languages interact with one another and their contextual meanings. It can handle sparsity and high-dimensional feature spaces well, which makes it appropriate for sarcasm detection in code-mixed, complicated, context-rich datasets. We have obtained an accuracy of 66.424% with F1 score of 0.321% in Tamil dataset and an accuracy of 71.019% in the Malayalam dataset
Decision Trees (DT) in sarcasm detection function by recursively segmenting the data according to the characteristics that most efectively distinguish sarcastic from non-sarcastic occurrences. These characteristics for texts with mixed codes could be word choice, punctuation, mood changes, and grammatical patterns in both languages. The feature providing the most information gain is chosen at each node in the tree, resulting in an organized sequence of choices that establish categorization.
Although DTs are easily interpreted and shown, a basic tree structure may not be able to properly capture the complex and context-dependent nature of sarcasm in code-mixed language. In these situations, feature engineering and the incorporation of DTs into ensemble models can enhance their eficacy in sarcasm detection. We have obtained an accuracy of 69.359% with F1 score of 0.403 in Tamil dataset and an accuracy of 77.17% in the Malayalam dataset.
The methodology involved comparing the actual results published on the website with the outcomes produced by each model. This approach facilitated a comprehensive assessment of the models’ performance relative to established benchmarks.Identification of sarcasm is essential, with Logistic Regression (LR) achieving the highest accuracies of 75.50% for Tamil and 83.62% for Malayalam. Despite its straightforward nature, LR efectively handled code-mixed data, delivering consistent performance across both languages. In comparison, K-Nearest Neighbors (KNN) struggled with context identification in code-switching scenarios, resulting in lower accuracies of 73.51% for Tamil and 76.39% for Malayalam. Decision Trees (DT) outperformed KNN in capturing context-based signals, while Support Vector Machines (SVM) demonstrated satisfactory performance with high-dimensional data, although both models faced dificulties in grasping the nuances of sarcasm. Ultimately, the incorporation of transliteration-based techniques and tailored machine learning models contributed to improved system accuracy, highlighting the potential for enhanced sarcasm detection across multilingual datasets.
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