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DOI: 10.14569/IJACSA.2018.090802
PDF

Recognition of Ironic Sentences in Twitter using Attention-Based LSTM

Author 1: Andrianarisoa Tojo Martini
Author 2: Makhmudov Farrukh
Author 3: Hongwei Ge

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 8, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Analyzing written language is an interesting topic that has been studied by many disciplines. Recently, due to the explosive growth of Internet, social media has become an attractive source of searching and getting information for research purposes on written communication. It is true that different words in a sentence serve different purposes of conveying the meaning while they are of different significance. Therefore, this paper is going to employ the attention mechanism to find out the relative contribution or significance of every word in the sentence. In this work, we address the problem of detecting whether a tweet is ironic or not by using Attention-Based Long Short-Term Memory Network. The results show that the proposed method achieves competitive performance on average recall and F1 score compared to the state-of-the-art results.

Keywords: Irony detection; attention; attention mechanism; sentiment analysis; long-short-term memory

Andrianarisoa Tojo Martini, Makhmudov Farrukh and Hongwei Ge, “Recognition of Ironic Sentences in Twitter using Attention-Based LSTM” International Journal of Advanced Computer Science and Applications(IJACSA), 9(8), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090802

@article{Martini2018,
title = {Recognition of Ironic Sentences in Twitter using Attention-Based LSTM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090802},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090802},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {8},
author = {Andrianarisoa Tojo Martini and Makhmudov Farrukh and Hongwei Ge}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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