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. **TextBlob**: TextBlob is a free and open-source API that uses machine learning algorithms to analyze text sentiment. It provides a simple and intuitive API for analyzing text data.

2. **Stanford CoreNLP**: Stanford CoreNLP is a popular open-source library for natural language processing (NLP) tasks, including sentiment analysis. It uses a variety of algorithms and models to analyze text data and provides a wide range of tools and techniques for sentiment analysis.

3. **VADER (Valence Aware Dictionary and sEntiment Reasoner)**: VADER is a sentiment analysis tool developed by Social Intelligence that uses a dictionary-based approach to analyze text sentiment. It is particularly useful for analyzing text data that may contain sarcasm, irony, or other forms of subtle sentiment.