Abstract

Aspect based sentiment analysis aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with respect to that aspect. This paper aims to give a solution for the FiQA 2018 challenge subtask 1. We perform aspect-based sentiment analysis on the microblogs and headlines of financial domain. We use a multi-channel convolutional neural network for sentiment analysis and a recurrent neural network with bidirectional long short-term memory units to extract aspect from a given headline or microblog. Our proposed model produces a weighted average F1 score of 0.69 for the aspect extraction task and predicts sentiment intensity scores with a mean squared error of 0.112 on 10-fold cross validation. We believe that the developed system has direct applications in the financial domain.