Package: sentida
SentidaThe new state-of-the-art Danish sentiment analysis tool further developed from the previous state-of-the-art Sentida and 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, for python along with Sentida and programmed loosely from theVADERsentiment analysis python implementation.Authors and CitationInstallationDocumentationContextNotes on the current state of the programReferencesAuthors and CitationCreated 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.comAarhus University,CINeMaSÃ¸ren Orm H.sorenorm@live.dkAarhus University,CINeMaInstallationYou can install Sentida through pip with the following command:pip install sentidaDocumentation and examplesThe 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 <0.2.1WARNING: 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 ContextThanks 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.TXTThe Sentida sentiment analysis tool is freely available for
research purposes (please cite Lauridsen et al., 2019). If you want to use the tool
for commercial purposes, please contact:
-contact@esbenkc.com-sorenorm@live.dkOr the Sentida team:
-gustavaarup0111@gmail.com-jacdals@hotmail.com-larskjartanbachersvendsen@gmail.comSENTIDA
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.NotesVADER BASIS VALUESMultiplication 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 BASIS VALUESCurrently 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 sentencesFUTURE IMPROVEMENTSStill 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 valuesReferencesLauridsen, 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