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Found 8 papers, 0 papers with code
TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddings
This paper describes the system used for detecting humor in text.
TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings
The results are better when compared to the model we developed in SemEval-2019 Task6.
SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data
This paper describes the work on mining the suggestions from online reviews and forums.
TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
The systems developed by TECHSSN team uses multi-level classification techniques.
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection
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.
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron
Sentiment analysis plays an important role in E-commerce.
SSN\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier
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.
SSN\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
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.
