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. **TextBlob**: TextBlob is an open-source API that uses a simple algorithm to determine the sentiment of text. It supports multiple languages and provides a sentiment intensity score between 0 (very negative) and 1 (very positive).

2. **VADER (Valence Aware Dictionary and sEntiment Reasoner)**: VADER is a rule-based sentiment analysis tool developed by Social Intelligence. It's a more advanced tool compared to TextBlob, and it can handle more complex sentiment analysis tasks.

3. **Stanford CoreNLP**: Stanford CoreNLP is a Java library for natural language processing. It includes a sentiment analysis tool that can analyze text data and provide sentiment intensity scores. It supports multiple languages and is widely used in academic and industrial applications.