| 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. |
