Exploration of Sentiments and Genre in Spanish American Novels

Ulrike Henny-Krahmer
(CLiGS, University of Würzburg)

June 29, 2018

Slides at: https://hennyu.github.io/dh_18/


  1. Sentiments and Genre: Background, Aims, Hypotheses
  2. Data: Spanish American Novels
  3. Methods: Sentiment Analysis & Machine Learning
  4. Results and Discussion

1. Sentiments and Genre: Background, Aims, Hypotheses

Sentiments and Genre

  • Sentiment Analysis: computational treatment of sentiment, opinion, emotion in text (Pang and Lee, 2008)
  • Sentiments modelled as polarity values or emotion values
  • Method has been used for genre analysis (Kim et al, 2017; Zehe et al., 2016)
  • Here: analysis of Spanish American novels

Aims & Hypotheses

  • Exploration of relationship between sentiments and genre in 19th c. Spanish-American novels
  • Sentiments as linguistic manifestations of emotions on the textual surface
  • Hypothesis 1: degree and kind of emotionality differs for different subgenres
  • Hypothesis 2: it matters, whether emotions are expressed in direct speech or narrated text
  • Sidegoal: test two sentiment lexica for Spanish

Sentence examples

from El Chacho by Eduardo Gutiérrez, 1884, military novel, narrated text:

Quiroga entretanto permanecía en Buenos Aires, bebiendo en la inspiración infame del tirano las más sangrientas ideas, y recibiendo las más terribles instrucciones.

Quiroga, in the meantime, stayed in Buenos Aires, drinking the most blodthirsty ideas from the infamous inspiration of the tyrant, receiving the most terrible instructions.

from Clemencia by Ignacio Manuel Altamirano, 1869, sentimental novel, direct speech:

— ¡Oh! sí podrá usted, Fernando, sí podrá usted. A una mujer tan hermosa como ésta, lo difícil, lo imposible es no amarla. Es demasiado encantadora para que el corazón de usted pueda permanecer indiferente.

Oh! yes, you can, Fernando, yes, you can. It's difficult, impossible not to love a woman as beautiful as this one. She is too charming for your heart to remain indifferent.

2. Data: Spanish American Novels

Data: Spanish American Novels

  • 30 novels, 1840-1910
  • 3 countries:
    Argentina (16)
    Cuba (9)
    Mexico (5)
  • 16 authors
  • 4 subgenres:
    sentimental (9)
    historical (8)
    sociopolitical (7)
    costumbrista (6)

Distribution of novels per decade and subgenre

3. Methods: Sentiment Analysis & Machine Learning

Methods: Sentiment features

Sentiment lexica:

SentiWordNet 3.0 NRC Emotion Lexicon
Miller, 1995; Baccianella et al., 2010 Saif and Turney, 2013
polarity (positive, negative, neutral) polarity + 8 basic emotions (Trust, Fear, Joy, Sadness, Anger, Disgust, Anticipation, Surprise)
117,653 entries 14,182 entries
  • Linguistic annotation
    FreeLing (Padró and Stanislovsky, 2012)
  • Separation of
    direct speech
    & narrated text
  • Sentiment values per sentence
  • Threshold: 1

Methods: Subgenre classification

  • Decision Tree Classifier: easily interpretable
  • sentences aggregated into 5 sections, divided by section length: 150 data points
  • 60 experiments: varying type of features/lexicon, depth of decision tree
  • 5-fold cross-validation

4. Results and Discussion

Sentence example

from Romualdo. Uno de tantos, by Francisco Calcagno, 1881, anti-slavery novel (narrated text)

SentiWordNet 3.0

La pluma no alcanza a describir las salvajes peripecias de aquella lucha espantosa.
The quill not accomplish to describe the ferocious events of that fight frightening.
- neutral - positive
- neutral - negative
- - neutral negative

NRC Emotion Lexicon

La pluma no alcanza a describir las salvajes peripecias de aquella lucha espantosa.
The quill not accomplish to describe the ferocious events of that fight frightening.
- neutral - neutral - neutral - negative - - - negative negative
Anger Anger Disgust
Fear Fear Fear

Results for subgenre classification

Feature importance

  • tree depth: 3
  • feature set: NRC speech
  • F1 training set: 0.75
  • F1 test set: 0.66
  • most important features:
    positive speech
    narrated fear

Decision tree

tree depth: 3, feature set: NRC speech, F1 training set: 0.75, F1 test set: 0.66

Comparison of classification methods

Conlusions and Next Steps

  • Classification by sentiment features: better understanding of how emotions are expressed linguistically in subgenres
  • Distinction narrated text – direct speech matters
  • For Spanish: NRC Emotion Lexicon better than SentiWordNet, relevance of 8 basic emotions
  • Next: increase corpus size, use other classifiers, combine sentiment and other types of features, analyze genre pairs, revise genre assignments

Thank you! - ¡Muchas gracias!

Slides at: https://hennyu.github.io/dh_18/

CLiGS: http://cligs.hypotheses.de/

CC-BY 4.0


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