Targeted Aspect-Based Sentiment Analysis for Ugandan Telecom Reviews from Twitter

In this paper we present SentiTel, a fine-grained opinion mining dataset that is human annotated for the task of targeted aspect-based sentiment analysis (TABSA). SentiTel contains Twitter reviews about three telecoms in Uganda posted in the period between February 2019 and September 2019. The reviews in the dataset have a code-mix of English and Luganda a language that is commonly spoken in Uganda. The dataset in this paper consists of 5973 human annotated reviews with the target entity which is the target telecom, aspect and sentiment towards the aspect of the target telecom. Each review contains at least one target telecom. Two models are trained for the TABSA task that is random forest which is the baseline model and BERT. The best results are obtained using BERT with an Area Under the ROC Curve (AUC) of 0.950 and 0.965 on aspect category detection and sentiment classification respectively. The results show that even though tweets are written without the intention of writing a formal review, they are rich in information and can be used for fine-grained opinion mining. Finally, the results show that fine-tuning the pre-trained BERT model on a downstream task generates better results compared to the baseline models.