Sentiment analysis is a research topic that analyzes opinions, attitudes, and emotions expressed in a given text. The methodology is to identify and extract subjective information by using context-mining techniques. The general purpose is to judge whether the potential emotions expressed are positive, negative, or neutral based on the source material. Many techniques, such as natural language processing (NLP), text analysis, computational linguistics, statistics, machine learning, and even biometrics, can be applied to sentiment analysis. So far, most users use Elasticsearch as the data store in sentiment analysis and the subsequent search or metric analysis. The workload for sentiment analysis is taken care of by third-party libraries. The following table introduces the two most commonly used libraries:
| Name | Programming language |
Description |
| TextBlob | Python... |
