For a long time, I wanted to write a summary of the conference that I attended. Not just to share the new ideas and trends prevalent, also to keep track of the thoughts that I had during the conference.
As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time. In this paper, we present an unsupervised …
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 …
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 …
Sentiment analysis or recognizing emotions from short and noisy text typically from social networks such as Twitter.
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.