T. T. Mirnalinee

Also published as: Mirnalinee T T, T T Mirnalinee


2022

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Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil
Varsini S | Kirthanna Rajan | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.

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SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model
Praveenkumar Vijayakumar | Prathyush S | Aravind P | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.

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SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models
Sarika Esackimuthu | Shruthi Hariprasad | Rajalakshmi Sivanaiah | Angel S | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.

2019

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TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
Angel Suseelan | Rajalakshmi S | Logesh B | Harshini S | Geetika B | Dyaneswaran S | S Milton Rajendram | Mirnalinee T T
Proceedings of the 13th International Workshop on Semantic Evaluation

Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google’s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.

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SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data
Rajalakshmi S | Angel Suseelan | S Milton Rajendram | Mirnalinee T T
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the work on mining the suggestions from online reviews and forums. Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice. The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (suggestion mining) uses a rule-based approach for feature selection, SMOTE technique for data augmentation and deep learning technique (Convolutional Neural Network) for classification. We have compared the results with Random Forest classifier (RF) and MultiLayer Perceptron (MLP) model. Results show that the CNN model performs better than other models for both the subtasks.

2018

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SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection
Angel Deborah S | Rajalakshmi S | S Milton Rajendram | Mirnalinee T T
Proceedings of The 12th International Workshop on Semantic Evaluation

The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build the model for emotion intensity ordinal classification, sentiment analysis ordinal classification and emotion classfication subtasks. Support Vector Machine was used to build the model for emotion intensity regression and sentiment intensity regression subtasks.

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SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron
Rajalakshmi S | Angel Deborah S | S Milton Rajendram | Mirnalinee T T
Proceedings of The 12th International Workshop on Semantic Evaluation

Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.

2017

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SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier
Angel Deborah S | S Milton Rajendram | T T Mirnalinee
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties.

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SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
Angel Deborah S | S Milton Rajendram | T T Mirnalinee
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

The system developed by the SSN_MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously.