README.md

metadata

license: apache-2.0
tags:
  - Finance-sentiment-analysis
  - generated_from_trainer
metrics:
  - f1
  - accuracy
  - precision
  - recall
model-index:
  - name: bert-base-finance-sentiment-noisy-search
    results: []
widget:
  - text: >-
      Third quarter reported revenues were $10.9 billion, up 5 percent compared
      to prior year and up 8 percent on a currency-neutral basis
    example_title: Positive
  - text: >-
      The London-listed website for businesses reported a pretax loss of $26.6
      million compared with a loss of $12.9 million the previous year
    example_title: Negative
  - text: Microsoft updates Outlook, Teams, and PowerPoint to be hybrid work ready
    example_title: Neutral

bert-base-finance-sentiment-noisy-search

This model is a fine-tuned version of bert-base-uncased on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below:

1. First "bert-base-uncased" was fine-tuned on Kaggle's finance news sentiment analysis https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news dataset achieving accuracy of about 88%
2. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed the most to the "Positive" and "Negative" classes by inspecting only bi-grams.
3. Using the top 25 bi-grams per class (i.e. "Positive" / "Negative") we invoked Bing news search with those bi-grams and retrieved up to 50 news items per bi-gram phrase.
4. We called it "noisy-search" because it is assumed the positive bi-grams (e.g. "profit rose" , "growth net") give rise to positive examples whereas negative bi-grams (e.g. "loss increase", "share loss") result in negative examples but note that we didn't test for the validity of this assumption (hence: noisy-search)
5. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations.
6. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set split.
7. Training with couple of thousands noisy "positives" and "negatives" examples yielded a test set accuracy of about 95%.
8. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from about 88% to more than 95%.

Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf:

https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing

Model description

BERT model trained on noisy data from search results. See PDF for more details.

Intended uses & limitations

Intended for use on finance news sentiment analysis with 3 options: "Positive", "Neutral" and "Negative" To get the best results feed the classifier with the title and either the 1st paragraph or a short news summarization e.g. of up to 64 tokens.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0