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dzone.com /2 weeks ago

Scalable Support Request Analysis Using Embeddings, HDBSCAN, and Tiny LLMs

Data Exploration Analyze the historical data to understand data quality, recurring key phrases, noise, and other patterns. Also, examine meta-attributes such as manual tagging, ass...

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r-bloggers.com /1 month ago

TALL â€“ Text Analysis for ALL, a new R Shiny app for NLP and Text Mining workflows

TALL â€“ Text Analysis for ALL is an R Shiny app that includes a wide set of methodologies specifically tailored for various text analysis tasks. It aims to address the needs of re...

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zonkafeedback.com /1 month ago

Mastering Thematic Analysis Using AI for Unlocking Faster CX Insights

TL;DR AI-powered thematic analysis uses NLP and large language models to cluster feedback into themes automatically, replacing weeks of manual coding with minutes of processing...

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medium.com /1 month ago

Why Raw Text Is the Wrong Data Layer for RAG (and What to Do Instead)

From messy files to retrieval-ready data: why your pipeline needs structured document processing.Continue reading on Medium »

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medium.com /3 weeks ago

Cleaning, Filtering, and Deduplicating Text Data: A Practical NLP Guide

At first glance, text data seems simple — just words, right? Maybe a few sentences, some reviews, support tickets, emails, web pages…Continue reading on Medium »

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kdnuggets.com /1 day ago

The ‘Entry-Level’ Gatekeeper: Auditing Job Descriptions with Textstat

This article shows how to use free, open-source tools like Python and its Textstat library to build a script that automates the process of capturing "gatekeeping language" in job d...

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kdnuggets.com /1 day ago

The ‘Entry-Level’ Gatekeeper: Auditing Job Descriptions with Textstat

This article shows how to use free, open-source tools like Python and its Textstat library to build a script that automates the process of capturing "gatekeeping language" in job d...

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testingxperts.com /3 weeks ago

NLP Applications in Business – Turning Unstructured Data into Operational Advantage

Unstructured data is where many enterprise decisions slow down. This blog explains how NLP applications in business help convert emails, chats, tickets, calls, and documents into a...

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towardsdatascience.com /1 week ago

Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale

For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Co...

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medium.com /18 hours ago

Text Classification at Scale: Why It’s Harder Than It Looks, and How to Think About It

From “just use an LLM” to building systems that stay reliable across thousands of categoriesContinue reading on Medium »

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medium.com /1 month ago

Understanding NLP Token Classification Tasks: From Words to Meaning

Natural Language Processing (NLP) is one of the most powerful areas of Artificial Intelligence. It enables machines to understand…Continue reading on Medium »

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marktechpost.com /1 month ago

A Coding Guide to Build Advanced Document Intelligence Pipelines with Google LangExtract, OpenAI Models, Structured Extr...

In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the requir...

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genengnews.com /1 month ago

Text Mining Culture Conditions and Glycosylation Relationships

An automated “text mining” and knowledge graph-based analytical approach can help biopharmaceutical process developers better understand how cell culture conditions influence glyco...

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blog.jetbrains.com /1 month ago

PyCharm: Using Bag-of-Words With PyCharm

Have you ever wondered how machine learning models actually work with text? After all, these models require numerical input, but text is, well, text. Natural language processing...

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dzone.com /1 day ago

LLM-Powered Deep Parsing for Industrial Inventory Search

Industrial ERPs often look structured on the surface: item IDs, purchase orders, stock levels. But in many companies, they are overloaded with unintentional duplicates because the...

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zonkafeedback.com /1 month ago

How AI Analyzes Open-Ended Feedback: From Themes to Signals

TL;DR AI qualitative data analysis goes beyond faster coding: purpose-built platforms extract themes, sentiment, effort, churn risk, intent, and entities from every open-ended...

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towardsdatascience.com /13 hours ago

Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval

Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acron...

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forrester.com /1 week ago

Findings from The Forrester Waveâ„¢: Document Mining And Analytics Platforms, Q2 2026

The Forrester Waveâ„¢: Document Mining And Analytics Platforms, Q2 2026 highlights a market that is broad, fragmented, and rapidly evolving â€” where success depends less on vendor...

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zonkafeedback.com /1 month ago

We Analyzed 1M+ Customer Feedback Responses. Here's What's Inside.

TL;DR Our analysis of 1M+ open-ended customer feedback responses across industries and 8 languages found that the average response contains 4.2 distinct topics: manual review c...

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kdnuggets.com /1 month ago

I Vibe Coded a Tool to That Analyzes Customer Sentiment and Topics From Call Recordings

Build an AI customer sentiment analyzer for call recordings using Whisper, BERTopic & Streamlit with this open-source step-by-step guide with code.

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bioengineer.org /1 month ago

Multi-Stacked BiLSTM Enhances Twitter Sentiment Analysis

In a groundbreaking advancement at the intersection of natural language processing and social media analytics, researchers Gomathi, Saranya, Munirathinam, and their team have unvei...

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towardsdatascience.com /1 month ago

I Built an AI Pipeline for Kindle Highlights

A local, zero-cost project that cleans, structures, and summarizes your reading automatically The post I Built an AI Pipeline for Kindle Highlights appeared first on Towards Data S...

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blog.apify.com /1 month ago

How to get Twitter data for sentiment analysis

Build an automated pipeline that collects Tweets, analyzes their sentiment, and delivers structured results - without writing code.

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habr.com /1 month ago

RAG: Как собрать свой ретривер для особых случаев

С опытом у RAG-инженера накапливается солидный багаж эвристик и инструментов, которые в определенных задачах превосходят по качеству или скорости стандартные. Фраза «а для этого у...

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