uhh-lt/ASAB

Amharic Sentiment Annotator Bot # ASAB Amharic Sentiment Annotator Bot (ASAB) #### ASAB: is the first of its kind to conduct surveys based on a specific reward scheme, which is mobile card vouchers. # Abstract **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. # Components ## Annotator ### Installation of ASAB ### User guide 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 vedi …