GenAI for Better NLP Systems II: Social Media Analytics

Author(s): Nabanita Roy

Originally published on Towards AI.

Leveraging GenAI for Social Media Analytics for swifter text processing and inferences as well as improving traditional NLP models with the aid of LLMs

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Generative AI, especially OpenAI’s ChatGPT, has revolutionized how we approach data. Leveraging this modern tool (or should I call it futuristic🤖), we can overcome many challenges in data-driven systems if used intelligibly. In my previous article, I demonstrated how we can use GenAI to generate synthetic data to boost performance for machine learning models for unbalanced data for Natural Language Processing (NLP) use-cases. This article shifts focus to social media analytics, exploring how GenAI can:

Accelerate NLP inferencesEnhance datasets for traditional modelsOvercome limitations of traditional NLP techniques with ease

Here, I have expanded on the most common NLP tasks in social media analytics and demonstrated the usage of prompting techniques to get LLMs to do the job. However, Large Language Models (LLMs) can either be used to solve the problem end-to-end or can be used for auxiliary tasks such as data labeling, feature extraction, or preliminary analysis, thus enhancing the process of building traditional models.

In each segment of this article, I’ve included a ‘💡 How to Use’ section that highlights how tasks performed using LLMs can be leveraged to support the development of traditional NLP models…. Read the full blog for free on Medium.

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Published via Towards AI