# FinVADER
**VADER sentiment classifier updated with financial lexicons**
**VADER** *(Valence Aware Dictionary and sEntiment Reasoner)* is a mainstream model for sentiment analysis using a
general-language human-curated lexicon, including linguistic features expressed on social media. As such, the model works
worse on texts that use domain-specific language, such as finance or economics.
**FinVADER** improves VADER's classification accuracy, including two finance lexicons: [SentiBignomics](https://github.com/consose/SentiBigNomics),
and [Henry's word list](https://journals.sagepub.com/doi/10.1177/0021943608319388). *SentiBigNomics* is a detailed financial lexicon for aspect-based sentiment analysis with
approximately 7300 terms containing a polarity score ranging in [-1,1] for each item. *Henry's lexicon* covers 189 words
appearing in the company earnings press releases.
FinVADER outperforms VADER on Financial PhraseBank data:
The code for this benchmark test is [here](https://github.com/PetrKorab/FinVADER/blob/main/finvader_benchmark.ipynb)
****
## Installation
FinVADER requires [Python 3.8 - 3.11](https://www.python.org/downloads/), and [NLTK](http://www.nltk.org).
To install using pip, use:
`pip install finvader`
## Usage
* **Import the library**:
``` python
from finvader import finvader
```
* **Select lexicons:**
``` python
def finvader(text = 'str', # Text
indicator = 'str', # VADER' indicator: 'pos'/'neg'/'neu'/'compound'
use_sentibignomics: bool= False, # Use SentiBignomics lexicon
use_henry: bool= False): # Use Henry's lexicon
)
```
* **Use the classifier:**
``` python
text = "The period's sales dropped to EUR 30.6 m from EUR 38.3 m, according to the interim report, released today."
scores = finvader(text,
use_sentibignomics = True,
use_henry = True,
indicator = 'compound' )
```
## Documentation, examples and tutorials
For examples of coding, read these tutorials:
**FinVADER: Sentiment Analysis for Financial Applications** [here](https://medium.com/python-in-plain-english/finvader-sentiment-analysis-for-financial-applications-6ab3c08840b4)
**Fine-tuning VADER Classifier with Domain-specific Lexicons** [here](https://medium.com/mlearning-ai/fine-tuning-vader-classifier-with-domain-specific-lexicons-1b23f6882f2)
---
Please visit [here](https://github.com/PetrKorab/finvader/issues) for any questions, issues, bugs, and suggestions.
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"description": "# FinVADER\r\n**VADER sentiment classifier updated with financial lexicons**\r\n\r\n\r\n**VADER** *(Valence Aware Dictionary and sEntiment Reasoner)* is a mainstream model for sentiment analysis using a \r\ngeneral-language human-curated lexicon, including linguistic features expressed on social media. As such, the model works\r\nworse on texts that use domain-specific language, such as finance or economics.\r\n\r\n**FinVADER** improves VADER's classification accuracy, including two finance lexicons: [SentiBignomics](https://github.com/consose/SentiBigNomics),\r\nand [Henry's word list](https://journals.sagepub.com/doi/10.1177/0021943608319388). *SentiBigNomics* is a detailed financial lexicon for aspect-based sentiment analysis with \r\napproximately 7300 terms containing a polarity score ranging in [-1,1] for each item. *Henry's lexicon* covers 189 words \r\nappearing in the company earnings press releases. \r\n\r\nFinVADER outperforms VADER on Financial PhraseBank data: \r\n\r\n\r\n\r\n\r\nThe code for this benchmark test is [here](https://github.com/PetrKorab/FinVADER/blob/main/finvader_benchmark.ipynb)\r\n\r\n**** \r\n\r\n\r\n## Installation\r\n\r\nFinVADER requires [Python 3.8 - 3.11](https://www.python.org/downloads/), and [NLTK](http://www.nltk.org). \r\n\r\nTo install using pip, use:\r\n\r\n`pip install finvader`\r\n\r\n\r\n## Usage\r\n\r\n* **Import the library**:\r\n\r\n\r\n``` python\r\nfrom finvader import finvader\r\n```\r\n\r\n* **Select lexicons:**\r\n\r\n\r\n``` python\r\ndef finvader(text = 'str', # Text\r\n indicator = 'str', # VADER' indicator: 'pos'/'neg'/'neu'/'compound' \r\n use_sentibignomics: bool= False, # Use SentiBignomics lexicon\r\n use_henry: bool= False): # Use Henry's lexicon\r\n) \r\n```\r\n\r\n* **Use the classifier:**\r\n\r\n``` python\r\ntext = \"The period's sales dropped to EUR 30.6 m from EUR 38.3 m, according to the interim report, released today.\"\r\n\r\nscores = finvader(text, \r\n use_sentibignomics = True, \r\n use_henry = True, \r\n indicator = 'compound' )\r\n```\r\n\r\n## Documentation, examples and tutorials\r\n\r\nFor examples of coding, read these tutorials:\r\n\r\n**FinVADER: Sentiment Analysis for Financial Applications** [here](https://medium.com/python-in-plain-english/finvader-sentiment-analysis-for-financial-applications-6ab3c08840b4)\r\n\r\n**Fine-tuning VADER Classifier with Domain-specific Lexicons** [here](https://medium.com/mlearning-ai/fine-tuning-vader-classifier-with-domain-specific-lexicons-1b23f6882f2)\r\n\r\n---\r\n\r\nPlease visit [here](https://github.com/PetrKorab/finvader/issues) for any questions, issues, bugs, and suggestions.\r\n",
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