senttimetritarkkuus

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the emotional tone or sentiment behind a series of words. The sentiment can be categorized as positive, negative, or neutral. Sentiment analysis can be performed at various levels of granularity, known as sentiment granularity. This refers to the level of detail at which sentiment is analyzed within a text.

At the most basic level, sentiment analysis can be performed at the document level, where the entire text is analyzed as a single unit. This method is useful for quickly determining the overall sentiment of a long piece of text, such as a book or a long article. However, it may not provide insights into the sentiment of specific sections or topics within the text.

At a more granular level, sentiment analysis can be performed at the sentence level. This approach allows for a more detailed analysis of the sentiment, as each sentence is analyzed individually. This method is useful for identifying shifts in sentiment within a text, such as a change from positive to negative sentiment.

At the most granular level, sentiment analysis can be performed at the aspect level. This approach involves analyzing the sentiment of specific aspects or features within a text. For example, in a product review, the sentiment of the product's design, price, and customer service can be analyzed separately. This method provides the most detailed insights into the sentiment of a text, but it also requires more complex NLP techniques.

The choice of sentiment granularity depends on the specific use case and the level of detail required. For example, a company may use document-level sentiment analysis to quickly monitor customer feedback, while a researcher may use aspect-level sentiment analysis to study the sentiment of specific topics within a large corpus of text.