A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis

Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya


Abstract
In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These are trained on top of pre-trained, autoencoder-based, financial word embeddings and lexicon features. An ensemble is constructed by combining these deep learning models and a classical supervised model based on Support Vector Regression (SVR). We evaluate our proposed technique on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The propose model shows impressive results on two datasets, i.e. microblogs and news headlines datasets. Comparisons show that our proposed model performs better than the existing state-of-the-art systems for the above two datasets by 2.0 and 4.1 cosine points, respectively.
Anthology ID:
D17-1057
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
540–546
Language:
URL:
https://www.aclweb.org/anthology/D17-1057
DOI:
10.18653/v1/D17-1057
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1057.pdf