Air pollution has emerged as a universal concern across the globe affecting human health. This increasing danger motivates the study of systems for predicting air pollutant severities ahead of time. In this paper, we have proposed the use of a …
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. …
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 …
Applicability of reinforcement learning (RL) algorithms to a class of problems rarely addressed in machine learning literature, involving the control of a dynamic system with high-dimensional control inputs (actions).
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 …
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 …
The impact of different types of events reported in News articles on stock market is a widely accepted phenomenon. Market analysts rely heavily on technology to combine data from different sources and generate appropriate insights for predicting …
The impact of different types of events reported in News articles on stock market is a widely accepted phenomenon. Market analysts rely heavily on technology to combine data from different sources and generate appropriate insights for predicting stock movements.