Stop words list generation contributes to reduction in the size of vector space of the corpus, indexing structure, high compression rate, speed up calculation and increasing the accuracy of Information Retrieval (IR) systems. The IR requires the matching of query to the most appropriate documents, which can cause additional memory overhead, low document recall and ambiguous results if a standard stop words list is not used. One of the current research works used intersection theory (IT) for aggregation of Frequency Analysis, Word Distribution Analysis and Word Entropy Measure for stop words generation. The intersection of set numbers is computed arbitrarily and difficult to manipulate for the generation of stop words. This study designed a Yoruba Stop Words Generator (YSWG) using Inclusion-Exclusion Principle to generalize the aggregated methods together with Term Frequency-Inverse Document Frequency. Each of the two methods generated their own stop words list after passing through text preprocessing stage including the diacritization of the Yoruba Language corpus. Cosine similarity measure was applied to the generated Yoruba and English languages stop words from the two methods as user query with the two corpuses. The YSWG system used Multinomial Naïve Bayes for updating the library of the system, especially in the event of an evolving new word. The YSWG was implemented alongside IT method using Python Programming Language and HyperText Markup Language with Unicode Transformation Format 8 bit (UTF-8) encoding standard for proper recognition and decoding of diacritized characters. A dataset of Yoruba corpus with 1,388,050 tokens and 40,212 distinct words that cuts across different domains was computed by the YSWG together with the IT method to determine the scalability of the system. When the corpus was passed through the IT method, it produced a precise, generalized and standardized 255 Yoruba Language stop words while the existing method produced 230 stop words. The YSWG was able to capture and preserve the character properties of diacritics of Yoruba language and non-diacritics of English language. The YSWG and IT methods were evaluated using precision, recall, accuracy, error, f-measure and execution time. The results of the study showed that YSWG performed better with precision, recall, accuracy, error, f-measure and execution time (95%, 98%, 99%, 0.005%, 0.96 and 0.1119ms) compared with the IT method values (83%, 91%, 86%, 0.02%, 0.86 and 0.1121ms) respectively. The results imply that YSWG had a better performance, efficient and more reliable than the IT method in generating stop words. A text compression rate of 65% was achieved after the removal of stop words generated by YSWG compared with 49% of the IT method. There was a higher cosine similarity value on YSWG than the IT method, which indicated that the new design would retrieve exact user query result. The YSWG method with the inclusion-exclusion principle effectively performed better than IT method in the categorization and identification of stop words.