👋 Welcome to the `pyFin-Sentiment` docs!

Installation

This library requires python>=3.8!

pip install pyfin-sentiment

Before you start...

This library is intended for extracting financial market sentiment from short-form social media posts like tweets.
Market sentiment is different from general sentiment:

Example: "My $AAPL short is going great!" has a positive generic sentiment but a negative market sentiment
If you are looking for a generic sentiment model that works well on social media content, take a look at VADER or TwitterRoBERTa
If you are looking for a sentiment analysis models that excels on new headlines sentiment analysis, check out FinBERT
Otherwise, stay here 🙃

Sentiment Labels

We use the following conventions for mapping sentiment classes:

Class Name	Meaning
1	Positive, Bullish
2	Neutral, Uncertain
3	Negative, Bearish

Models

For now, we support the following models. You'll need to specify the correct model_name when downloading and loading models.

`model_name`	Description
`"small"` (default)	A logistic regression trained on the TF-IDF representation of the sub-word tokenized texts

MWE

A minimum working example for the impatient:

from pyfin_sentiment.model import SentimentModel

# this only needs to be downloaded once:
SentimentModel.download("small")

model = SentimentModel("small")
model.predict(["Long $TSLA!!", "Selling my $AAPL position"])
# array(['1', '3'], dtype=object)

The `SentimentModel` class

There's actually only one class you'll really need: the SentimentModel class

About

This library was developed by me (Moritz Wilksch) as part of my master thesis. If you experience any issues feel free to raise them at the project's GitHub repo.

👋 Welcome to the pyFin-Sentiment docs!