Generated Key Phrases For Cyber Bullying

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Generated Key Phrases For Cyber Bullying Quotes
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Bullying doesn’t always happen at school or in person. A lot of bullying takes place over the internet. Cyber bullying has been defined as ‘‘willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices’’ (Hinduja & Patchin,2009). When kids get caught bullying at physically at school they then take it to electronic devices where it is harder for them to get caught. They produce face accounts on websites to torment and torture kids that are weaker than them, such as making fake accounts on Facebook or MySpace. In this study it says “There is a difficulty of detecting the misbehavior, identifying the offending party, proving of verifying the wrongdoing” (Hinduja, S., &Patchin, J.W. 2010). We need to set rules and make sure that cyber bullying comes to an end. People that bully others should face severe consequences. We should have laws and rules that lean heavily against bullying. I believe that schools should hold more meetings for teachers to educate kids on the severe outcomes of bullying. Schools are becoming better at educating kids on the effects of bullying, “Fortunately, schools have strategies to prevent bullying. These strategies are most effective when they are part of a comprehensive prevention program implemented at the district, school, and classroom levels. Research indicates that schools can cut bullying by as much as 50% with a comprehensive school wide

Mar 15, 2011 View and download cyber bullying essays examples. Also discover topics, titles, outlines, thesis statements, and conclusions for your cyber bullying essay. Key terms: cyberbullying, bullying, interventions, student perceptions. The words and phrases are familiar enough; high school students across the country hear these insults being. A extreme form of bullying where physical assaults are recorded on mobile to others Online chat Words/actions, that have not been welcomed, and intend to annoy, alarm or abuse individual Cyber-Victim The application of a set of criteria against which internet content is judged acceptable or not Spam A social networking website where you can share. Chang Su & Thomas J. Holt - Cyber bullying in Chinese Web Forums: An examination of nature and extent International Journal of Cyber Criminology Vol 4 Issue 1&2 January - July 2010 / July - December 2010.

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Cyber bullying also referred to as social online cruelty can be described as an intentional aggressive act which is carried out by an individual or group of individuals against a victim done repeatedly over a long period of time and sent through electronic contacts. Cyber bullying is usually repeated over time unless it is a death threat. Cyber-bullying is poised to turn into the biggest online concern, already affecting up to 35% of all children Dr Martyn Wild Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.

The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.

This capability is useful if you need to quickly identify the main points in a collection of documents. For example, given input text 'The food was delicious and there were wonderful staff', the service returns the main talking points: 'food' and 'wonderful staff'.

For more information, see Supported languages.

Tip

Generated Key Phrases For Cyber Bullying Quotes

Text Analytics also provides a Linux-based Docker container image for key phrase extraction, so you can install and run the Text Analytics container close to your data.

Preparation

Key phrase extraction works best when you give it bigger amounts of text to work on. This is opposite from sentiment analysis, which performs better on smaller amounts of text. To get the best results from both operations, consider restructuring the inputs accordingly.

You must have JSON documents in this format: ID, text, language

Document size must be 5,120 or fewer characters per document, and you can have up to 1,000 items (IDs) per collection. The collection is submitted in the body of the request. The following example is an illustration of content you might submit for key phrase extraction.

Step 1: Structure the request

For information about request definition, see How to call the Text Analytics API. The following points are restated for convenience:

Create a POST request. Review the API documentation for this request: Key Phrases API.
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Set the HTTP endpoint for key phrase extraction by using either a Text Analytics resource on Azure or an instantiated Text Analytics container. You must include /text/analytics/v2.1/keyPhrases in the URL. For example: https://<your-custom-subdomain>.api.cognitiveservices.azure.com/text/analytics/v2.1/keyPhrases.
Set a request header to include the access key for Text Analytics operations.
In the request body, provide the JSON documents collection you prepared for this analysis.

Tip

Use Postman or open the API testing console in the documentation to structure a request and POST it to the service.

Step 2: Post the request

Analysis is performed upon receipt of the request. For information about the size and number of requests you can send per minute or per second, see the data limits section in the overview .

Recall that the service is stateless. No data is stored in your account. Results are returned immediately in the response.

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Step 3: View results

All POST requests return a JSON formatted response with the IDs and detected properties. The order of the returned key phrases is determined internally, by the model.

Output is returned immediately. You can stream the results to an application that accepts JSON or save the output to a file on the local system, and then import it into an application that allows you to sort, search, and manipulate the data.

An example of the output for key phrase extraction is shown here:

As noted, the analyzer finds and discards non-essential words, and it keeps single terms or phrases that appear to be the subject or object of a sentence.

Summary

In this article, you learned concepts and workflow for key phrase extraction by using Text Analytics in Cognitive Services. In summary:

Key phrase extraction API is available for selected languages.
JSON documents in the request body include an ID, text, and language code.
POST request is to a /keyphrases endpoint, using a personalized access key and an endpoint that is valid for your subscription.
Response output, which consists of key words and phrases for each document ID, can be streamed to any app that accepts JSON, including Microsoft Office Excel and Power BI, to name a few.

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