Semantic analyses are essential for understanding the meaning behind the text.

Technical analyses are essential for making informed investment decisions.

Cluster analyses are commonly used in data science to identify patterns and relationships in large datasets.

Meta-analyses are commonly used in medical research to combine and analyze data from multiple studies.

Asymptotic analyses are used to analyze the behavior of a function as the input approaches a certain value.

Factor analyses are commonly used in social sciences to examine the underlying factors of a phenomenon.

Sentiment analyses are used to analyze the emotions and opinions expressed in text data.

Discourse analyses play a crucial role in understanding social interactions.

The researchers conducted several qualitative analyses to examine the themes in the data.

Quantitative analyses are essential for understanding the data.