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Leveraging user-generated social media content with text-mining examples

IBM Journey to AI blog

With nearly 5 billion users worldwide—more than 60% of the global population —social media platforms have become a vast source of data that businesses can leverage for improved customer satisfaction, better marketing strategies and faster overall business growth. words and/or phrases) known as tokens.

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Flag harmful content using Amazon Comprehend toxicity detection

AWS Machine Learning Blog

Online communities are driving user engagement across industries like gaming, social media, ecommerce, dating, and e-learning. Use cases Text moderation plays a crucial role in managing user-generated content across diverse formats, including social media posts, online chat messages, forum discussions, website comments, and more.

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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

Flipboard

We use two AWS Media & Entertainment Blog posts as the sample external data, which we convert into embeddings with the BAAI/bge-small-en-v1.5 Deploy the BAAI/bge-small-en-v1.5 However, you can check their official website or social media accounts for any updates on when the center will reopen. embeddings.

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AI in the publishing Industry

How to Learn Machine Learning

With the rise of social media and other digital channels, publishers must distribute their content across multiple platforms. One such tool is social media automation. Social media automation uses AI algorithms to schedule posts and publish content across multiple social media platforms.

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Flag harmful language in spoken conversations with Amazon Transcribe Toxicity Detection

AWS Machine Learning Blog

The increase in online social activities such as social networking or online gaming is often riddled with hostile or aggressive behavior that can lead to unsolicited manifestations of hate speech, cyberbullying, or harassment. MediaFileUri – Enter the URI location of the audio file on Amazon S3.

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Spear phishing vs. phishing: what’s the difference?

IBM Journey to AI blog

credit card numbers or social security numbers), downloading malware, visiting malicious websites, sending money to the wrong people, or otherwise themselves, their associates or their employers. a global brand), sent en masse to millions of people in hope that some small percentage of recipients will take the bait.

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Artificial Intelligence in Tourism Industry

How to Learn Machine Learning

Chatbots can be found on websites, social media platforms, and messaging apps, and they can provide 24/7 customer service without the need for human operators. These recommendations can be delivered through email, push notifications, or social media ads.