String Kernels for Polarity Classification: A Study Across Different Languages

@inproceedings{GimnezPrez2018StringKF,
  title={String Kernels for Polarity Classification: A Study Across Different Languages},
  author={Rosa M. Gim{\'e}nez-P{\'e}rez and Marc Franco-Salvador and Paolo Rosso},
  booktitle={NLDB},
  year={2018}
}
The polarity classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong… 
1 Citations
Data analysis on music classification system and creating a sentiment word dictionary for Kokborok language
TLDR
This work shows the development of a lexicon for a poorly resourced language, namely Kokborok, and creates a sentimental word dictionary known as lexicons to develop a polarity classification system.

References

SHOWING 1-6 OF 6 REFERENCES
Single and Cross-domain Polarity Classification using String Kernels
TLDR
This work detects the lexical peculiarities that characterise the text polarity and maps them into a domain independent space by means of kernel discriminant analysis in single and cross-domain polarity classification.
Can characters reveal your native language? A language-independent approach to native language identification
TLDR
An approach that uses character n-grams as features is proposed for the task of native language identification and has an important advantage in that it is language independent and linguistic theory neutral.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
TLDR
This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.
Thumbs up? Sentiment Classification using Machine Learning Techniques
TLDR
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Enriching Word Vectors with Subword Information
TLDR
A new approach based on the skipgram model, where each word is represented as a bag of character n-grams, with words being represented as the sum of these representations, which achieves state-of-the-art performance on word similarity and analogy tasks.
Generalized Discriminant Analysis Using a Kernel Approach
TLDR
A new method that is close to the support vector machines insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space to deal with nonlinear discriminant analysis using kernel function operator.