Metadata-Version: 2.1
Name: sentida
Version: 0.4.3
Summary: The Sentida Danish sentiment analysis package
Home-page: https://github.com/esbenkc/emma
Author: Esben Kran, Søren Orm
Author-email: contact@esbenkc.com
License: MIT
Description: #### Sentida V2
        The new state-of-the-art Danish sentiment analysis tool upgraded from the previous state-of-the-art Sentida to V2. Sentida V2 shows significant improvement in classifying sentiment in text compared to Sentida (p < 0.01) in three different validation datasets (TP, TP2, Emma).
        
        Implementation of the previous state-of-the-art Danish SA in R, [Sentida](https://github.com/guscode/sentida), for python along with SentidaV2 and programmed loosely from the [VADER](https://github.com/cjhutto/vaderSentiment) sentiment analysis python implementation.
        
        - [Authors and Citation](#authors-and-citation)
        - [Installation](#installation)
        - [Documentation](#documentation-and-examples)
        - [Context](#acknowledgments-and-context)
        - [Notes on the current state of the program](#notes)
        - [References](#references)
        
        ### Authors and Citation
        Created by SÃ¸ren Orm and Esben Kran.
        > Emma: Danish Computational Analysis of Emotion in Text
        > (by S. Orm and E. Kran)
        
        For questions and commercial use, please contact:
        * Esben Kran C.
          * contact@esbenkc.com
          * Aarhus University, [CINeMa](https://inema.webflow.io)
        * SÃ¸ren Orm H.
          * sorenorm@live.dk
          * Aarhus University, [CINeMa](https://inema.webflow.io)
        
        ### Installation
        You can install SentidaV2 through pip with the following command:
        ```
        pip install sentida
        ```
        ### Documentation and examples
        The function:
        ```
        from sentida import Sentida
        Sentida().sentida(
                            text,
                            output = ["mean", "total", "by_sentence_mean", "by_sentence_total"],
                            normal = True,
                            speed = ["normal", "fast"]
                            )
        # Speed parameter does not have an effect in version <0.2.1
        ```
        WARNING: Setting speed to fast drastically reduces sentiment precision in complex sentences but speeds up the process by 180% (10,000 iteration test).
        
        Usage examples:
        ```
        # Define the class:
        SV = Sentida()
        _____________________________
        
        SV.sentida(
                text = 'Lad der blive fred.',
                output = 'mean',
                normal = False)
        
        Example of usage:
        Lad der bliver fred
        Sentiment =  2.0
        _____________________________
        
        SV.sentida(
                text = 'Lad der blive fred!',
                output = 'mean',
                normal = False)
        
        With exclamation mark:
        Lad der blive fred!
        Sentiment =  3.13713
        _____________________________
        
        SV.sentida(
                text = 'Lad der blive fred!!!',
                output = 'mean',
                normal = False)
        
        With several exclamation mark:
        Lad der blive fred!!!
        Sentiment =  3.7896530399999997
        _____________________________
        
        SV.sentida(
                text = 'Lad der BLIVE FRED',
                output = 'mean',
                normal = False)
        
        Uppercase:
        lad der BLIVE FRED
        Sentiment =  3.466
        _____________________________
        
        SV.sentida(
                text = 'Det gÃ¥r dÃ¥rligt.',
                output = 'mean',
                normal = False)
        
        Negative sentence:
        Det gÃ¥r dÃ¥rligt
        Sentiment =  -1.8333333333333335
        _____________________________
        
        SV.sentida(
                text = 'Det gÃ¥r ikke dÃ¥rligt.',
                output = 'mean',
                normal = False)
        
        Negation in sentence:
        Det gÃ¥r ikke dÃ¥rligt
        Sentiment =  1.8333333333333335
        _____________________________
        
        SV.sentida(
                text = 'Lad der blive fred, men det gÃ¥r dÃ¥rligt.',
                output = 'mean',
                normal = False)
        
        'Men' ('but'):
        Lad der blive fred, men det gÃ¥r dÃ¥rligt
        Sentiment =  -1.5
        _____________________________
        
        SV.sentida(
                text = 'Lad der blive fred.',
                output = 'mean',
                normal = True)
        
        Normalized:
        Lad der blive fred
        Sentiment =  0.4
        _____________________________
        
        SV.sentida(
                text = 'Lad der bliver fred. Det gÃ¥r dÃ¥rligt!',
                output = 'by_sentence_mean',
                normal = False)
        
        Multiple sentences mean:
        Lad der bliver fred. Det gÃ¥r dÃ¥rligt!
        Sentiments = [2.0, -2.8757025]
        _____________________________
        
        SV.sentida(
                text = 'Lad der bliver fred. Det gÃ¥r dÃ¥rligt!',
                output = 'by_sentence_total',
                normal = False)
        
        Multiple sentences total:
        Lad der bliver fred. Det gÃ¥r dÃ¥rligt!
        Sentiments = [2.0, -5.751405]
        _____________________________
        ```
        ### Acknowledgements and Context
        Thanks to CINeMa (https://inema.webflow.io),
        the Sentida team, jry, VADER, AFINN, and last
        but not least Formula T., for inspiration and encouragement.
        For license information, see LICENSE.TXT
        
        The SentidaV2 sentiment analysis tool is freely available for
        research purposes (please cite). If you want to use the tool
        for commercial purposes, please contact:
            - contact@esbenkc.com
            - sorenorm@live.dk
        Or the SentidaV1 team:
            - gustavaarup0111@gmail.com
            - jacdals@hotmail.com
            - larskjartanbachersvendsen@gmail.com
        
        SENTIDA v2.
        Aarhus University, Cognitive Science.
        2019 - Cognition & Communication.
        @authors: sorenorm & esbenkc.
        
        This script was developed along with other tools in an attempt to improve
        danish sentiment analysis. The tool will be updated as more data is collected
        and new methods for more optimally accessing sentiment is developed.
        
        ### Notes
        VADER BASIS VALUES
        
        Multiplication values:
            0.291, 0.215, and 0.208 for !, !!, and !!! respectively
                empirically tested by one sentence compared to the three conditions
            0.733 for uppercase
                empirically tested from single control sentence to uppercase version
            0.293 for degree modifications from adverbs
                empirically tested with "extremely"
        
        
        SENTIDA V2 BASIS VALUES
        
        Currently using VADER basis values
        Question mark is: XXX
        Degree modifications for other words are implemented in intensitifer list
            - Need implementation of larger intensifier list based on sentences
        
        
        FUTURE IMPROVEMENTS
        
        Still missing: common phrases, adjusted values for exclamation marks,
        Adjusted values for men-sentences, adjusted values for uppercase,
        More rated words, more intensifiers/mitigators, better solution than snowball stemmer,
        Synonym/antonym dictionary.
        Social media orientated: emoticons, using multiple letters - i.e. suuuuuper.
        Normalization with respect to sub-(-1) and super-(1) output values
        
        ### References
        Lauridsen, G. A., Dalsgaard, J. A., & Svendsen, L. K. B. (2019). SENTIDA: A New Tool for Sentiment Analysis in Danish. Journal of Language Works - Sprogvidenskabeligt Studentertidsskrift, 4(1), 38â€“53.
        
        Hutto, C. J., & Gilbert, E. (2014, May 16). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Eighth International AAAI Conference on Weblogs and Social Media. Eighth International AAAI Conference on Weblogs and Social Media. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109
Keywords: Natural Language Processing Sentiment Analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
