Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets

@inproceedings{Sidorov2012EmpiricalSO,
  title={Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets},
  author={Grigori Sidorov and Sabino Miranda-Jim{\'e}nez and Francisco Viveros Jim{\'e}nez and Alexander F. Gelbukh and No{\'e} Alejandro Castro-S{\'a}nchez and Francisco Velasquez and Ismael D{\'i}az-Rangel and Sergio Su{\'a}rez Guerra and Alejandro Trevi{\~n}o and Juan Gordon},
  booktitle={MICAI},
  year={2012}
}
Opinion mining deals with determining of the sentiment orientation—positive, negative, or neutral—of a (short) text. Recently, it has attracted great interest both in academia and in industry due to its useful potential applications. One of the most promising applications is analysis of opinions in social networks. In this paper, we examine how classifiers work while doing opinion mining over Spanish Twitter data. We explore how different settings (n-gram size, corpus size, number of sentiment… CONTINUE READING
Highly Cited
This paper has 83 citations. REVIEW CITATIONS
43 Extracted Citations
15 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 43 extracted citations

84 Citations

01020201320142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 84 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 15 references

Twitter sentiment classification using distant supervision

  • A. Go, R. Bhayani, L. Huang
  • CS224N Project Report,
  • 2009
Highly Influential
4 Excerpts

Weighted Spanish Emotion Lexicon

  • I. Díaz-Rangel, G. Sidorov, S. Suárez-Guerra
  • 2012
2 Excerpts

Similar Papers

Loading similar papers…