This study investigated the comparative performance of two popular sentiment analysis tools, Azure Sentiment Analysis and Google Cloud Sentiment Analysis, applied to Polish and English text data. The analysis focused on two content types: hotel reviews and tweet comments. The findings demonstrate that the type of text and language analyzed significantly influence the accuracy of sentiment detection. Sentiment analysis tools performed worse on informal and unstructured text like tweet comments compared to structured and formal text like hotel reviews. Additionally, language can influence the performance of sentiment analysis tools. In this study, English showed a slight edge over Polish, particularly for tweet comments. When analyzing tweet comments, the Google Cloud Sentiment Analysis function outperformed Azure Sentiment Analysis in both Polish and English, suggesting better handling of informal and ambiguous language. Furthermore, this research addressed the limitations of relying solely on pre-assigned sentiment labels from analysis tools. A custom labeling method was developed that effectively captures the nuances of sentiment in both Polish and English content. This method provides a more granular and objective approach to sentiment classification and offers a valuable framework for further research in sentiment analysis. By highlighting the challenges associated with informal and unstructured text, as well as the importance of considering language variations, this research contributes to the field of sentiment analysis. The findings and the proposed labeling method present valuable opportunities for future research along promising directions, particularly in sentiment-based decision-making for social media data analysis.