An Empirical Study on Machine Learning-Based Sentiment Classification Using Polarity Clues

Title: An Empirical Study on Machine Learning-Based Sentiment Classification Using Polarity Clues
Authors: Ulli Waltinger
Pub/Conf:  Web Information Systems and Technologies Lecture Notes in Business Information Processing Volume 75, 2011, pp 202-214

Abstract:
In recent years a variety of approaches in classifying the sentiment polarity of texts have been proposed. While in the majority of approaches the determination of subjectivity or polarity-related term features is at the center, the number of publicly available dictionaries is rather limited. In this paper, we investigate the performance of combining lexical resources with machine learning based classifier for the task of sentiment classification.We systematically analyze four different English and three different German polarity dictionaries as a resources for a sentiment-based feature selection. The evaluation results show that smaller but more controlled dictionaries used for feature selection perform within a SVM-based classification setup equally good compared to the biggest available resources.

BibTeX:

@inproceedings{2276415,
  author       = {Waltinger, Ulli},
  editor       = {Mylopoulos W.M.P. Aalst van der and J. Mylopoulos and M. Rosemann and M.J. Shaw and C. Szyperski},
  issn         = {1865-1348},
  language     = {English},
  location     = {Valcencia, Spain},
  pages        = {202--214},
  publisher    = {Springer},
  series       = {Lecture Notes in Business Information Processing (LNBIP)},
  title        = {An Empirical Study on Machine Learning-based Sentiment Classification Using Polarity Clues},
  volume       = {75},
  year         = {2011},
}

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