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. **Natural Language Toolkit (NLTK)**: A popular Python library for Natural Language Processing (NLP) tasks, including sentiment analysis. It provides pre-trained models and tools for text processing, tokenization, and sentiment analysis.

2. **Vader Sentiment Analysis Tool**: A free, open-source tool developed by Paul Dai, Kyle Simpson, and Chris Manning. It uses a weighted algorithm called the VADER (Valence Aware Dictionary and sEntiment Reasoner) to analyze the sentiment of text.

3. **TextBlob**: A simple, open-source API that provides a simple and easy-to-use interface for sentiment analysis. It uses a set of predefined rules to classify text as positive, negative, or neutral, and can also generate a sentiment score based on the text.

These tools can be used to analyze text data and determine the sentiment or emotional tone behind it.