SARCASM DETECTION MODEL FOR AMHARIC TEXT

Tsegaw, Miniybel

dc.contributor.author	Tsegaw, Miniybel
dc.date.accessioned	2020-03-16T09:15:30Z
dc.date.available	2020-03-16T09:15:30Z
dc.date.issued	2020-03-16
dc.identifier.uri	http://hdl.handle.net/123456789/10350
dc.description.abstract	Sentiment Analysis is a technique to identify people‟s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations; services sarcasm, entailment, etc. Sarcasm is a special kind of sentiment that comprise of words, which mean the opposite of what you really want to say (especially to insult or wit someone, to show irritation, or to be funny). People often express it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. Which is obviously not available for expressing sarcasm in text? This is a crucial step to sentiment analysis, considering the prevalence and challenges of sarcasm in sentiment-bearing text. Sarcasm detection is the task of predicting sarcasm in text. Therefore, in this thesis we developed a model to detect the presence of sarcasm in Amharic texts. We used primary data‟s from “Abebe Tolla‟s” “Mitsetoch”, “Silaqoch” and “Shimutoch” essay books and his official facebook blogs. The rest of the data is collected by using FacePager API from other Facebook blogs, and pages, which write about sarcasm elements, and to support the data any related reference such as magazines, newspapers and Amharic literature are used as a dataset. We used lexical (unigram), Semantic and Emoticons (smiley faces etc) features to extract different feature sets as useable inputs for Machine learning. Support Vector Machine (SVM), Neural Network (NN) and Random forest classifiers trained on simple lexical dictionary based approach is used to classify the sarcastic Amharic texts based on the features provided. An accuracy of 80.6%, 80.1 and 79% was obtained on the total collected datasets with the Support Vector Machine, Neural Network, and Random Forest classifier respectively. We found some strong features that characterize sarcastic texts. However, a combination of more subtle dictionary-based features proved more promising in identifying the various facets of sarcasm.	en_US
dc.language.iso	en	en_US
dc.subject	Computer Science	en_US
dc.title	SARCASM DETECTION MODEL FOR AMHARIC TEXT	en_US
dc.type	Thesis	en_US