Abstract
People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Naïve Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences.
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Notes
- 1.
Given a set of tweets the task consist in determining whether the user has expressed a positive, negative or neutral sentiment; more information is available at: http://alt.qcri.org/semeval2015/task11/.
- 2.
Using emoticons, with few characters is possible to display one’s true feeling; sometimes they are virtually required under certain circumstances in text-based communication, where the absence of some kind of cues can hide what was originally intended to be humorous, sarcastic, ironic, and often negative [14].
- 3.
Feature previously applied by Reyes et al. [10].
- 4.
The complete list of words can be downloaded from http://users.dsic.upv.es/grupos/nle.
- 5.
- 6.
https://codegoogle.com/p/ws4j/. This module allows to calculate a set of seven different similarity measures.
- 7.
DAL is composed by 8,000 English words, distributed in three categories: Activation, refers to the degree of response, either passive or active, that humans exhibit in an emotional state; Imagery, quantifies how easy or difficult is to form a mental picture for a given word; and Pleasantness, quantifies the degree of pleasure suggested by a word.
- 8.
- 9.
- 10.
We used Weka toolkit’s version of each classifier available at http://www.cs.waikato.ac.nz/ml/weka/downloading.html.
- 11.
Default parameters for each algorithm were used.
- 12.
We performed experiments using each similarity measure of the WordNet::Similarity module. Due to lack of space, we report only the results with highest classification rates. The similarity measures are described in detail in [9].
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Acknowledgments
The National Council for Science and Technology (CONACyT Mexico) has funded the research work of the first author (Grant No. 218109/313683, CVU-369616). The research work of third author was carried out in the framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLI CATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.
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Hernández-Farías, I., Benedí, JM., Rosso, P. (2015). Applying Basic Features from Sentiment Analysis for Automatic Irony Detection. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_38
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