In the heart of Swahili: An exploration of data collection methods and corpus curation for natural language processing.

Swahili corpus is a dataset generated by collecting written Kiswahili sentences from different sectors that deals with Kiswahili documents. Corpus of intended language is needed in Natural Language Processing (NLP) task to fit algorithm in order to understand that language before training the model. Swahili corpus dataset generated contained 1,693,228 sentences with 39,639,824 words and 871,452 unique words. Corpus exported in text file format with storage size of 168 MB. These sentences collected from different sources in different categories as follows: - Health (AFYA), Business and Industries (BIASHARA), Parliament (BUNGE), Religion (DINI), Education (ELIMU), News (HABARI), Agriculture (KILIMO), Social Media (MITANDAO), Non-Governmental Organizations (MASHIRIKA YA KIRAIA), Government (SERIKALI), Laws (SHERIA) and Politics (SIASA). the systematic data collection process employed for the creation of a Swahili corpus derived from multiple public websites and reports. The compilation of this corpus involves a meticulous and comprehensive approach to ensure the representation of diverse linguistic contexts and topics relevant to the Swahili language. The data collection process commenced with the identification of suitable sources across various domains, including news articles, health publications, online forums, and Governmental public reports. Websites and platforms with publicly available Swahili content were systematically crawled and archived to capture a broad spectrum of linguistic expressions. Furthermore, special attention was given to reputable sources to maintain the authenticity of the corpus and linguistic richness. The inclusion of diverse sources ensures that the corpus reflects the linguistic nuances inherent in different contexts and registers within the Swahili language. Additionally, efforts were made to incorporate variations in domain dialects, acknowledging the linguistic diversity present in Swahili. The potential for reusing this Swahili corpus is vast. Researchers, linguists, and language enthusiasts can leverage the diverse and extensive dataset for a multitude of applications, including NLP tasks such as sentiment analysis, textual data clustering, classifications tasks and machine translation. The Corpus can serve as training data for developing and evaluating NLP algorithms, including part-of-speech tagging, and named entity recognition. Also, text mining techniques can be applied to corpus and enable researchers to extract valuable insights, identify patterns, and discover knowledge from large textual datasets.