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 project aims to give a solution for the FiQA 2018 challenge subtask 1.
Given a text instance in the financial domain (microblog message, news statement or headline) in English, detect the target aspects which are mentioned in the text (from a pre-defined list of aspect classes) and predict the sentiment score for each of the mentioned targets. Sentiment scores will be defined using continuous numeric values ranged from -1(negative) to 1(positive).
Model is evaluated on the basis of precision, recall and F1-score for aspect classification approaches and regard to MSE and R Squared(R^2) metrics for sentiment prediction.
An example of the input/output of the task is defined below:
"55": {
"sentence": "Tesco Abandons Video-Streaming Ambitions in Blinkbox Sale",
"info": [
{
"snippets": "['Video-Streaming Ambitions']",
"target": "Blinkbox",
"sentiment_score": "-0.195",
"aspects": "['Corporate/Stategy']"
},
{
"snippets": "['Tesco Abandons Video-Streaming Ambitions ']",
"target": "Tesco",
"sentiment_score": "-0.335",
"aspects": "['Corporate/Stategy']"
}
]
}
If you happen to refer my work please cite our research paper: Link
BibTeX Cite:
@article{salunkhe2019aspect, title={Aspect based sentiment analysis on financial data using transferred learning approach using pre-trained BERT and regressor model”}, author={Salunkhe, Ashish and Mhaske, Shubham}, year={2019} }