Emotion Detection for Afaan Oromo Using Deep Learning

Emotion detection in text has become more popular due to its various useful applications in a different area, such as tracking product perception, public opinion detection about political tendencies, stock market monitoring, text summarization, information extraction, recommendation system, question answering and etc. However, manually identifying emotion of a million of people and aggregating them towards a rapid and efficient decision is quite a challenging task due to the rapid growth of social media user.  This study aimed to develop Afaan Oromo emotion detection model in order to tackle this challenge.  This study adopts artificial neural network approach. We used python tools with Keras library. We conduct our experiments on five emotion class (anger(arii), love(jaalala), joy(gamachu), disgust(jibba), and sadness(gadda)) by collecting a total of 1005 emotional sentence of Afaan Oromo language that have been manually annotated. The sentence has been scraped from different official Facebook page such as Oromia Broadcasting Network (OBN) pages, Fana Broadcasting Corporation (FBC) Afaan Oromo page, and British Broadcasting Corporation (BBC) Afaan Oromo pages using Facepager tools by creating Facebook API id. After collecting these data all preprocessing steps like tokenization, stop word removal and normalization have been undertaken. We used word embedding’s for feature extraction of preprocessed data. Subsequently, we have applied three artificial neural network algorithms such as Feed forward neural network, long short-term Memory and Bidirectional long short-term memory for classification purpose of the vectorized sentence into their emotion class. We compared the three artificial neural network algorithms and found out that Bidirectional long short-term memory achieved the best performance. We have achieved an average accuracy of 66%, 78%, 83% using Feed Forward Neural Network, Long Short-Term Memory and Bidirectional Long Short-Term Memory respectively. Based on experimental result, the researcher concluded that increasing amount of dataset, tuning hyper parameters properly and trying by different algorithms can, in some case, improve the performance of the model. Keywords: Emotion Identification, Afaan Oromo, Artificial Neural Network, Social Media DOI: 10.7176/NMMC/92-01 Publication date:August 31st 202

Prevalence and the associated risk factors of gastrointestinal parasites of shoats in Tiyo District, Arsi zone, Oromia, South West Ethiopia

A cross-sectional study was conducted during October 2018 to November 2019 in Tiyo district, Arsi Zone of Oromia National Regional State in order to determine the prevalence of Shoats GIT parasites and the associated risk factors. A total of 384 fecal samples were collected from all animals. The collected samples were kept in cold chain and transported to Asella Regional Veterinary Laboratory. The quantitative method of fecal examination was conducted by using a modified McMaster egg-counting technique. The overall prevalence of GIT parasites was 70.5% (271/384). The prevalence of GIT parasites in sheep and goats were 52.8% (150/284) and 64% (64/100) respectively. The most GIT parasites found in the study area was Strongyle (39%), Stronglyoid (21%), Trichuris(11%) Paramphistomum (74%), Monezia (6%) Coccidia (25%) and mixed infection (121%). The degree of light, medium and severe GIT infestations in the study area was, 88(22.9%), 56(14.5%) and 75(19.5%) respectively. There was no significant association of parasites infestations with district, species, age and body conditions of the animals (P&gt;0.05). This study showed that, GIT parasites are the major health problems of small ruminants in the study area. Therefore, a further study on species of GIT parasites circulating in the area is crucial. Cost effective deworming strategy and awareness creations to the farmers in the study area should be advised.</p