Affect

Learning representations for sentiment classification using Multi-task framework

Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings. This paper postulates a generalized representation that collates training on multiple datasets using a Multi-task learning framework. …

TCS Research at SemEval-2018 Task 1 Learning Robust Representations using Multi-Attention Architecture

This paper presents system description of our submission to the SemEval-2018 task-1, Affect in tweets for the English language. We combine three different features generated using deep learning models and traditional methods in support vector …

Sentiment extraction from Consumer-generated noisy short texts

Sentiment analysis or recognizing emotions from short and noisy text from social networks such as twitter has been a challenging task. Most of the existing models use word level embeddings for the final classification of the sentiments. This paper …

Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets

This paper describes our approach to the Emotion Intensity shared task. A parallel architecture of Convolutional Neural Network (CNN) and Long short term memory networks (LSTM) alongwith two sets of features are extracted which aid the network in …

Sentiment Analysis of Short text

Sentiment analysis or recognizing emotions from short and noisy text typically from social networks such as Twitter.