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 used for sentiment analysis:

1. **VADER (Valence Aware Dictionary and sEntiment Reasoner)**: A popular open-source tool for sentiment analysis that uses a dictionary-based approach to identify the emotional tone of text. It's widely used in natural language processing (NLP) and machine learning (ML) applications.

2. **TextBlob**: A simple and easy-to-use API-based sentiment analysis tool that provides a sentiment score for a given piece of text. It uses a combination of natural language processing (NLP) techniques and machine learning algorithms to analyze the sentiment.

3. **Stanford CoreNLP**: A widely used, open-source tool for NLP that includes a sentiment analysis component. It uses rule-based and machine learning approaches to analyze the sentiment of text and identify emotions such as happiness, sadness, anger, and fear.