Three tools used for sentiment analysis are Lexalytics, OpinionFinder, and Google Cloud Natural Language. Lexalytics uses statistical language models and machine learning algorithms to identify sentiment in large-scale text data. OpinionFinder is a text mining tool designed to detect sentiment in web-based text data. Google Cloud Natural Language is a cloud-based platform that uses natural language processing and machine learning to analyze sentiment in text.
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Name three tools used for sentiment analysis

### Response:
Three tools used for sentiment analysis are Lexalytics, OpinionFinder, and Google Cloud Natural Language. Lexalytics uses statistical language models and machine learning algorithms to identify sentiment in large-scale text data. OpinionFinder is a text mining tool designed to detect sentiment in web-based text data. Google Cloud Natural Language is a cloud-based platform that uses natural language processing and machine learning to analyze sentiment in text.
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Here are three tools commonly used for sentiment analysis:

1. **IBM Watson Natural Language Understanding (NLU)**: This is a cloud-based NLP service that uses machine learning algorithms to analyze text data and identify sentiment. It can analyze text from various sources, including social media, emails, and web pages.

2. **TextBlob**: TextBlob is a free, open-source library that provides a simple API for sentiment analysis. It uses a set of rules-based algorithms to determine the sentiment of a piece of text, such as positive, negative, or neutral.

3. **Stanford CoreNLP**: Stanford CoreNLP is a popular open-source NLP library that includes tools for sentiment analysis. It uses a variety of machine learning algorithms, including decision trees and support vector machines, to analyze text data and determine sentiment.