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A Comprehensive Review on Summarizing Financial News Using Deep Learning

arXiv 2109.10118 ↗First posted 21 Sep 2021cs.CLcited by 0

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Abstract (arXiv)

Investors make investment decisions depending on several factors such as fundamental analysis, technical analysis, and quantitative analysis. Another factor on which investors can make investment decisions is through sentiment analysis of news headlines, the sole purpose of this study. Natural Language Processing techniques are typically used to deal with such a large amount of data and get valuable information out of it. NLP algorithms convert raw text into numerical representations that machines can easily understand and interpret. This conversion can be done using various embedding techniques. In this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT, GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM. This work aims to evaluate these model's performance to choose the robust model in identifying the significant factors influencing the prediction. During this research, it was expected that Deep Leaming would be applied to get the desired results or achieve better accuracy than the state-of-the-art. The models are compared to check their outputs to know which one has performed better.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 101 references that are in the map. First posted on arXiv 21 Sep 2021.

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18 of 101 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2014Distributed Representations of Sentences and Documentscited by 678
  4. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
  5. 2016"What is Relevant in a Text Document?": An Interpretable Machine Learning Approachcited by 53
  6. 2019Fine-grained Sentiment Classification using BERTcited by 38
  7. 2016Sentiment Analysis of Twitter Data for Predicting Stock Market Movementscited by 34
  8. 2020Adversarial Training for Aspect-Based Sentiment Analysis with BERTcited by 26
  9. 2019Recurrent Neural Networks for Time Series Forecastingcited by 19
  10. 2020A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and Newscited by 16
  11. 2018Overcoming the vanishing gradient problem in plain recurrent networkscited by 10
  12. 2016Stock trend prediction using news sentiment analysiscited by 9
6 more
  1. 2018Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Networkcited by 6
  2. 2020ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Predictioncited by 5
  3. 2020Global Sentiment Analysis Of COVID-19 Tweets Over Timecited by 2
  4. 2019Learning to Determine the Quality of News Headlinescited by 1
  5. 2020Word frequency and sentiment analysis of twitter messages during Coronavirus pandemiccited by 1
  6. 2020Sentiment Analysis on Customer Responsescited by 1

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