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VADER sentiment classifier updated with financial lexicons

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# FinVADER
**VADER sentiment classifier updated with financial lexicons**

**VADER** *(Valence Aware Dictionary and sEntiment Reasoner)* classifier 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:

![finvader_accuracy](https://github.com/PetrKorab/FinVADER/assets/62357254/6f464bb2-1d9c-4cb7-ba63-f535c6a1fda6)
![vader_accuracy](https://github.com/PetrKorab/FinVADER/assets/62357254/6bc4080b-ce1a-499f-9dbd-de8cf8f1ecdc)

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`

## Data requirements

It requires complete text data without NaN values and empty strings. Remove them in the pre-processing part.

## Usage

* **Import the library**:

``` python
from finvader import finvader
```

* **Select lexicons:**

``` python
def finvader(text = 'str',                    # Text
             indicator = 'str',               # VADER's 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

### Example of using the classifier:

``` python
import pandas as pd                                            # read data
data = pd.read_csv("ecb_speeches.csv")
```

``` python
from finvader import finvader                         
data['finvader'] = data.contents.apply(finvader,               # apply FinVADER and create a new column in data df
                                   use_sentibignomics = True,  # Use Lexicon 1
                                   use_henry = True,           # Use Lexicon 2
                                   indicator="compound")       # Use VADER's compound indicator
```

****

For examples of coding, read these  tutorials:

**FinVADER: Sentiment Analysis for Financial Applications** [here](https://python.plainenglish.io/finvader-sentiment-analysis-for-financial-applications-6ab3c08840b4?sk=01b880558bd66b83b44618051e2e5df4)

**Fine-tuning VADER Classifier with Domain-specific Lexicons** [here](https://medium.com/mlearning-ai/fine-tuning-vader-classifier-with-domain-specific-lexicons-1b23f6882f2?sk=f36e92bc46ba2997e1fc5f4fe2c44bcc)
****

Please visit [here](https://github.com/PetrKorab/finvader/issues) for any questions, issues, bugs, and suggestions.

FinVADER

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