TASSA Naive-Bayes strategy for sentiment analysis on Spanish tweets

  1. Pablo Gamallo 1
  2. Marcos Garcia 2
  3. Santiago Fernández-Lanza 1
  1. 1 Universidade de Santiago de Compostela
    info

    Universidade de Santiago de Compostela

    Santiago de Compostela, España

    ROR https://ror.org/030eybx10

  2. 2 CILENIS, S.L.
Book:
XXIX Congreso de la Sociedad Española de Procesamiento de Lenguaje Natural: SEPLN 2013
  1. Alberto Díaz Esteban (coord.)
  2. Iñaki Alegria Loinaz (coord.)
  3. Julio Villena Román (coord.)

Publisher: Sociedad Española para el Procesamiento del Lenguaje Natural

ISBN: 978-84-695-8349-4

Year of publication: 2013

Pages: 126-132

Congress: Sociedad Española para el Procesamiento del Lenguaje Natural. Congreso (29. 2013. Madrid)

Type: Conference paper

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Abstract

This article describes the strategy underlying the system presented by our team for the sentiment analysis task at TASS 2013. The system is mainly based on a naive-bayes classifier for detecting the polarity of Spanish tweets. The experiments have shown that the best performance is achieved by using a binary classifier distinguishing between just two sharp polarity categories: positive and negative. To identify more polarity levels, the system is provided with experimentally set thresholds for detecting strong, average, and weak (or neutral) values. In addition, in order to detect tweets with and without polarity, the system makes use of a very basic rule that searchs for polarity words within the analysed text. Evaluation results show a good performance of the system (about 67% accuracy) when it is used to detect four sentiment categories.

Data source: Dialnet