SENTIMENT ANALYSIS FOR AFAAN OROMOO USING COMBINED CONVOLUTIONAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

Sentiment analysis has become the most popular research topic due to its various application in business, politics, entertainment, however analyzing opinion of people from short text such as Twitter message and single sentence is quite a challenging task due to their informality, misspell and semantic error. In this study, we propose character level multiscale sentiment analysis for Afaan Oromoo using combined Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) approach. Since there is no standardized and sufficient corpus prepared for Afaan Oromoo Natural Language Processing (NLP) task including sentiment analysis so far, we have collected data from two domain, Facebook and Twitter for the experiment. After collecting data, we removed user names, links, none Afaan Oromoo texts, and any unnecessary characters. The cleaned data were annotated manually by 4 different annotators into five class namely, 2 ,1, -2, -1, and 0 which represent very positive, positive, very negative, negative and neutral respectively. This multi-scale sentiment analysis provides a more refined analysis, which is vital for prioritizing and comparison of different opinion. Afterward we performed experiments on the prepared corpus from Facebook and Twitter by applying Convolutional Neural Network, Bidirectional Long Short-Term Memory and combined Convolutional Neural Network-Bidirectional Long Short-Term Memory with character level word embedding. The experimental result validate that the proposed model outperforms both CNN and Bi-LSTM in both Facebook and Twitter dataset. Based on the implemented Facebook dataset we achieved a promising performance accuracy of 93.3%, 91.4%, and 94.1% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. Consequently, we executed twitter dataset and achieved 92.6%, 90.3%, 93.8% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. The result suggests the possibility of multi-scale sentiment analysis as well as CNN-Bi-LSTM on Afaan Oromoo. We have also suggested that the accuracy can be improved by building standardized and sufficient amount of data set, which was one of the most difficult and demanding tasks of our work