Deep Learning Based Emotion Detection Model for Amharic Text
| dc.contributor.advisor | Belay, Ayalew (PhD) | |
| dc.contributor.author | Tesfu, Eyob | |
| dc.date.accessioned | 2022-06-16T06:30:36Z | |
| dc.date.accessioned | 2023-11-29T04:06:42Z | |
| dc.date.available | 2022-06-16T06:30:36Z | |
| dc.date.available | 2023-11-29T04:06:42Z | |
| dc.date.issued | 2021-08-26 | |
| dc.description.abstract | Emotions are so important that whenever we need to make a decision, we want to feel other‟s emotions. This is not only true for individuals but also for organizations. Due to the rapid growth of internet peoples expirees their emotions using different social media networks, reviews, blogs, online and so on. The need for finding relevant sources, extracts related sentences with emotion, summarizes them and organize them to useful form is becoming very high. Emotion detection can play an important role in satisfying these needs. The process of emotion detection involves categorizing emotional sentences into predefined categories such as sadness, anger, disgust, happiness, so on based on the emotional terms that appear within the comment. So that it‟s difficult to manually identifying emotion of a million of users and aggregating them towards a rapid and efficient decision is quite a challenging task due to the rapid growth of Amharic language usage in social media. In this research work, an emotion detection model is proposed for determining the emotion expressed in the Amharic texts or comment. In this study, we proposed deep learning based emotion detection model for Amharic text using CNN with word embedding. The proposed model includes different tasks. The first task is text pre-processing which consists of commonly used text pre-processing tasks in many natural language processing applications. We perform text pre-processing in Amharic text and train the document using a word embedding in order to generate word embedding model. The embedding result provides a contextually similar word for every word in the training set then we implement our CNN model for emotion classification. The common evaluation metrics such as accuracy, recall, F1 score and precision were used to measure our proposed model performance. Deep learning based emotion detection model for Amharic text prototype is developed and used to tests the system performance using the collected Amharic text comments. Finally, this study with four categories (sadness, anger, disgust, and happiness) of classification shows a result of 71.11% accuracy. Also did better when the number of classification is two (positive and negative) shows result of 87.46% accuracy. We also evaluate our model using RNN to compare with our CNN model. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/32029 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Emotion Detection | en_US |
| dc.subject | Pre-Processing | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | CNN | en_US |
| dc.subject | RNN | en_US |
| dc.title | Deep Learning Based Emotion Detection Model for Amharic Text | en_US |
| dc.type | Thesis | en_US |