ASPECT-BASED SENTIMENT ANALYSIS FOR AMHARIC AND ENGLISH COMMENTS FROM ETHIO TELECOM FACEBOOK AND TWITTER PAGES USING DEEP LEARNING

Abstract

A vital tool for comprehending public opinion in a variety of fields, such as customer service and business decision-making, is sentiment analysis. In this study, user comments from Ethio Telecom Facebook and Twitter pages in both Amharic and English are analyzed for sentiment. The primary aim is to classify these comments into distinct sentiment categories such as positive, negative, or neutral, providing actionable insights to improve customer satisfaction and service delivery. This work was to develop a bilingual sentiment analysis model using written comments from Ethiopian telecom platforms on Facebook and Twitter in both Amharic and English To address the unique linguistic and morphological challenges of Amharic, the study incorporates specialized preprocessing steps, tokenization methods, and embedding’s. A balanced dataset of annotated comments in both languages is compiled for training and evaluation. The results demonstrate the effectiveness of deep learning models in capturing sentiment across both languages, achieving high accuracy and robustness. A total of 13,389 comments were collected, preprocessed, and manually labeled. In terms of language distribution, 52.91% (7,084 comments) were in pure Amharic, 28.75% (3,850 comments) in pure English, and 18.34% (2,455 comments) were mixed-language comments. Data sampling techniques, feature extraction using word representation techniques like Word2Vec, GloVe, and FastText, and deep learning architectures like Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) were all used in the study. Metrics like accuracy, precision, recall, and F1-score were used to evaluate the models, and by achieving an accuracy of 74.38% and an F1-score of 74.12% in the train test split, LSTM was the best performer. While GRU models showed lower performance with accuracies of 73.67% and an F1-score of 70.62% in the 80% training and 20% of the dataset test set. The LSTM model demonstrated the most consistent and robust performance train-test splitting methods, making it the best choice for this bilingual sentiment analysis task. Based on these experimental results, the LSTM model with train test split is recommended for analyzing the sentiment of bilingual social media comments, ensuring consistent and generalizable results.