Summarize the following text into a brief overview: Sentiment analysis, also known as opinion mining, is a natural language processing technique that involves identifying and extracting subjective information, such as opinions, emotions, and attitudes, from written text or speech. It is widely used in various applications, including market research, social media monitoring, customer service, and product reviews. Sentiment analysis algorithms typically use machine learning models trained on labeled datasets to classify the sentiment expressed in a given text as positive, negative, or neutral. Advanced techniques also consider contextual information, such as sarcasm and negation, to improve accuracy. Sentiment analysis provides valuable insights into public opinion and customer satisfaction, enabling organizations to make data-driven decisions and improve their products or services.
Summarize the following text into a brief overview: Sentiment analysis, also known as opinion mining, is a natural language processing technique that involves identifying and extracting subjective information, such as opinions, emotions, and attitudes, from written text or speech. It is widely used in various applications, including market research, social media monitoring, customer service, and product reviews. Sentiment analysis algorithms typically use machine learning models trained on labeled datasets to classify the sentiment expressed in a given text as positive, negative, or neutral. Advanced techniques also consider contextual information, such as sarcasm and negation, to improve accuracy. Sentiment analysis provides valuable insights into public opinion and customer satisfaction, enabling organizations to make data-driven decisions and improve their products or services.
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Sentiment analysis, or opinion mining, is an NLP technique that identifies and extracts subjective information like opinions, emotions, and attitudes from text or speech. It uses machine learning models to classify sentiment as positive, negative, or neutral, and considers context like sarcasm and negation. Sentiment analysis finds applications in market research, social media monitoring, customer service, and product reviews, providing insights into public opinion and customer satisfaction to support data-driven decision-making.
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Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Sum these three sentences up into one. 

A Scottish ultra-marathon runner was disqualified from a race after she used a car during part of the marathon route before taking home the trophy for third place.

Joasia Zakrzewski acknowledged that she made a “massive error” in accepting the trophy during the GB Ultras Manchester to Liverpool 50-mile race on April 7, BBC Scotland reported.

The record-setting runner told the outlet that she was feeling sore and started limping when she saw a friend on the side of the course who offered to give her a lift.
Response: A Scottish ultra-marathon runner named Joasia Zakrzewski was disqualified, even after accepting a trophy for third place, due to having used a car for part of the marathon route.

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.
