Natural Language Toolkit (NLTK) Sentiment Analysis Quick Reference

Natural Language Toolkit (NLTK) Sentiment Analysis Quick Reference.


Author(s): Adam Ross Nelson

Originally published on Towards AI.

Insights to Data Science: Journey Through Sentiment Analysis

One of the characters I write about in my books is named Poleh. Pol for short. Poleh sounds like Paula.

Before going into data science, Poleh was a seasoned marketing professional known for her intuitive understanding of client needs and her knack for crafting compelling campaigns. She had an uncanny ability to glean insights from client meetings, feedback sessions, and survey questions, focus groups, etc.

Image Credit: The author’s illustration was created in Canva. The caption reads, “Poleh as she ‘thinks’ about sentiment analysis. A woman working at a laptop with a thought bubble above her.

But as her career grew, so did… Read the full blog for free on Medium.

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Published via Towards AI


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.