In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.
THE PROBLEM OF TRANSLATING METAPHOR IN AN ARTISTIC TEXT (ON THE MATERIAL OF RUSSIAN AND ARABIC LANGUAGES)
The scientific studies that deal with Herminutia (interpretation) as the art of reading the interpretation practiced by the recipient after his understanding of the literary texts and works of art that he sees or read them so that these readings to make the act of reading and allow him the opportunity to mature and rational reflection of each text or artistic work.
Based on this, the researchers considered the establishment of the problem of their research through the search for the problematic overlap of concepts in the interpretive practices of the literary text?
The second chapter dealt with the definition of the term interpretation as well as interpretation as a theory and concept, and then the indicators reached by t
... Show MoreThe modern textual study researched the textuality of the texts and specified for that seven well-known standards, relying in all of that on the main elements of the text (the speaker, the text, and the recipient). This study was to investigate the textuality of philology, and the jurisprudence of the science of the text.
Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as t
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