In this article
Introduction to Sentiment Analysis
Sentiment analysis is a critical component of text analytics, which aims to determine the emotional tone of written text. It helps in understanding the speaker's emotions or attitudes towards specific topics, whether they are positive, negative, or neutral/mixed. By identifying these underlying emotions, businesses and organizations can gain valuable insights into their target audience's perceptions and preferences.
Automated Sentiment Analysis and Its Advantages
Automated sentiment analysis offers several advantages over manual sentiment tagging by humans, including increased efficiency, consistency, and scalability. It is particularly effective when analyzing unsolicited feedback (e.g., social media posts) or open-ended survey questions that are neutrally worded and encourage descriptive answers. However, its accuracy may decrease when analyzing questions with non-neutral wording or those that discourage rich responses.
Accuracy of Sentiment Analysis
While humans themselves may disagree on the sentiment of a piece of text 20-30% of the time, industry experts consider an 80% or higher accuracy rate for automated sentiment analysis to be good. As artificial intelligence continues to advance, it is expected that the accuracy of automated sentiment analysis will further improve, eventually reaching human-like levels of accuracy.
Interpreting Sentiment Analysis Results
When interpreting sentiment analysis results, it is essential to consider the accuracy of the analysis and the potential implications of any inaccuracies. For example, with an 80% accuracy rate, one in five responses may be miscategorized when examined individually. However, in most applications, we look at large volumes of data, and the law of large numbers suggests that the overall sentiment across a sufficiently large group will be accurate. Sentiment analysis for single responses is still valuable, but it is important to remember the potential for inaccuracies.
Category Sentiment and Fragment Size
In category sentiment analysis, the engine identifies the text related to a specific category and applies fragments, or windows, around the text to determine the emotional context. There is an option to adjust the Fragment Size (FS) in Model Builder on the Model Overview page to fine-tune the analysis.
Sentiment Analysis for Categories, Subcategories, and Attributes
Sentiment scores for categories, subcategories, and attributes are individually evaluated based on their respective expressions. As a result, a category may not have the same sentiment score as its subcategories. Additionally, when analyzing categories or subcategories with attributes, the attribute expression is not factored into the sentiment calculation for the category or subcategory. Instead, attribute sentiment is calculated based on the expression for both the category and the attribute.