Covid-19 Sentiment Modeling Using Machine Learning Natural Language Processing

Covid-19 Sentiment Modeling Using Machine Learning Natural Language Processing.

Overview

Author(s): Ashutosh Malgaonkar

Originally published on Towards AI.


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Sentiment Analysis of Covid Tweets

I am using Google Collaboratory for this. First, we install kaggle into Python using the following python code in your notebook.

!pip install kaggle

Next, go to kaggle.com. On kaggle.com, head over to settings by clicking your profile image at the top right corner of the page. Once you are on the settings page, click ‘create token’ under API and download the kaggle.json file.

It is time to upload kaggle.json into google collaboratory. Run this below code.

from google.colab import files# Upload the kaggle.json file that you downloaded earlieruploaded = files.upload()

This code above will give you an upload… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.