College of Computing and Informatics
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College of Computing and Informatics
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Item SENTIMENT ANALYSIS FOR AMHARIC-ENGLISH CODE-MIXED SOCIO-POLITICAL POSTS USING DEEP LEARNIN(WOLKITE UNIVERSITY, 2024-05) YITAYEW EBABUSentiment analysis is crucial in natural language processing for identifying emotional nuances in text. Analyzing sentiment in natural language text is essential for discerning emotional subtleties. However, this task becomes especially intricate when dealing with code-mixed texts, like Amharic-English, which exhibit language diversity and frequent code switching, particularly in social media exchanges. In this investigation, we propose employing CNN, LSTM, BiLSTM, and CNN-BiLSTM models to tackle sentiment classification in such code-mixed texts. Our approach involves leveraging deep learning techniques and various preprocessing methods, including language detection and code switching integration. We conducted four experiments utilizing Count Vectorizer and TF IDF. Our assessment reveals that incorporating language detection and code-switching significantly boosts model accuracy. Specifically, the average accuracy of the CNN model increased from 82.004% to 84.458%, the LSTM model from 79.716% to 81.234%, the BiLSTM model from 81.586% to 83.402%, and the CNN-BiLSTM model from 82.128% to 84.765%. These results underscore the efficacy of tailored preprocessing strategies and language detection in enhancing sentiment classification accuracy for code-mixed texts. Our study emphasizes the imperative of addressing language diversity and code-switching to achieve dependable sentiment analysis in multilingual environments. Furthermore, it provides valuable insights for future research, highlighting the importance of language specific preprocessing techniques to optimize model performance across diverse linguistic contexts