Amharic Sentiment Annotator Bot (ASAB)

Sentiment analysis is an important NLP application. As the number of social media users is ever-increasing, social media platforms would like to understand the latent meaning and sentiments of a text to enhance decision-making procedures. However, low resource languages such as Amharic have received less attention due to several reasons such as lack of well-annotated datasets, unavailability of computing resources, and fewer or no expert researchers in the area.

This work addresses three main research questions. We first explore the suitability of existing tools for the sentiment analysis task. There are no tools to support large-scale annotation tasks in Amharic. Also, the existing crowdsourcing platforms do not support Amharic text annotation. Hence, we build a social-network-friendly annotation tool called ASAB using the Telegram bot.

We have collected 9.4k tweets, where each tweet is annotated by three Telegram users. Secondly, we explore the suitability of machine learning approaches for Amharic sentiment analysis. The FLAIR deep learning text classifier, based on network embeddings that are computed from a distributional thesaurus, outperforms other supervised classifiers. Lastly, we have investigated the challenges in building a sentiment analysis system for Amharic and we found that the widespread usage of sarcasm and figurative speech are the main issues in dealing with the problem.

Details about the ASAB annotator tools are available in the ASAB annotator.

The annotation instruction and examples are available here.

If you want to test ASAB (without rewards, obviously), you can access it from this telegram application link.

A one minute vedio in Youtube shows you how to interqct with ASAB.

The ML classification models are described under model directory

The annotated data (Tweet_ID and label) are found under data

You can get the paper here and the poster here

If you use these resources and methods, please cite the following paper:

@InProceedings{yimametalcoling2020,
    title = "Exploring {A}mharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models",
    author = "Yimam, Seid Muhie  and
      Alemayehu, Hizkiel Mitiku  and
      Ayele, Abinew  and
      Biemann, Chris",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    pages = "1048--1060"
}