Mikimi/bert-base-case-financial-news-twitter-sentiment

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Description for the model on Hugging Face: A model for analyzing the tone of financial messages can be used to classify messages into three categories: bullish, bearish, and neutral tone. Here's how it might work:

▎1. Bullish Sentiment. Bullish sentiment messages usually contain positive words and phrases that indicate expectations of rising prices or improving economic conditions. Examples of such messages might include:

• “Shares of XYZ Company are expected to rise after a successful quarterly report.”
• “Analysts forecast oil prices to rise due to increased demand.”

▎2. Bearish Sentiment. Bearish sentiment messages, on the other hand, contain negative words and phrases that indicate expectations of falling prices or deteriorating economic conditions. Examples of such messages might include:

• “Economic data points to a possible recession, which could negatively impact the markets.”
• “ABC Company is facing losses and the stock may fall.”

▎3. Neutral Sentiment (Neutral Sentiment) Neutral messages do not express clear optimism or pessimism. They may contain factual information without assessing future changes. Examples of such messages might include:

• “There was little price movement in the market today.”
• “XYZ Company announced the launch of a new product but did not provide sales projections.”

Classification results can be used to analyze market sentiment and make investment decisions.

"LABEL_0": "Bearish" "LABEL_1": "Bullish" "LABEL_2": "Neutral"

Model Details

"LABEL_0": "Bearish" "LABEL_1": "Bullish" "LABEL_2": "Neutral"

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Mikhail Luk
• Model type: Financial Model
• Language(s) (NLP): English
• License: Free

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Uses

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Classification results can be used to analyze market sentiment and make investment decisions.

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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