A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis
By Md. Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya. 2017
94 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shuai Wang
- B. Liu
- Lei Zhang
- Bing Liu
- Omer Berat Sezer
- M. Ugur Gudelek
- William Yang Wang
- Ting-Hao 'Kenneth' Huang
In Management Science and Operations Research
- M. Ugur Gudelek
- Omer Berat Sezer
- A. Ozbayoglu
- B. Liu
- William Yang Wang
- Weiwei Jiang
- Longbing Cao
- Samuel Rönnqvist
Citations in the corpus
- Deep Learning for Financial Applications : A Survey (2020, methodology): “[185] compared CNN, LSTM and GRU based DL models against MLP for financial sentiment analysis.”
- Deep learning for sentiment analysis: A survey (2018, background): “Akhtar et al. 61 proposed several multi-layer perceptron based ensemble models for fine-gained sentiment classification of financial microblogs and news.”
- FinQA: A Dataset of Numerical Reasoning over Financial Data (2021, methodology): “…detect fraud (Han et al., 2018; Wang et al., 2019; Nourbakhsh and Bang, 2019), sentiment analysis to assist market prediction (Day and Lee, 2016; Wang et al., 2013; Akhtar et al., 2017), opinionated Question Answering (Liu et al., 2020), such as the FiQA2 dataset built from forums and social media.”