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What is Web Data Miner?

The Internet stands as the largest collection of publicly accessible resources in the world. At present, there are over 100 million websites that collectively feature more than 80 billion individual webpages. Each passing second witnesses a remarkable increase in the number of these webpages. Among this vast pool of content, users have access to a diverse range of valuable information, including contact information for prospective clients, competitive pricing details, real-time financial news, public opinion analysis, testimonials, trends in supply and demand, scholarly articles, online discussions, blogs, news articles, and the latest updates. However, the essential information is often buried within the complex HTML formats of these sites, which tend to be only partially organized. This complexity presents significant challenges for extracting and utilizing the data effectively. Additionally, navigating through this colossal amount of information requires advanced tools and strategic approaches to truly capitalize on its vast potential. As a result, the need for innovative solutions to streamline data extraction and enhance accessibility is more critical than ever.

What is Dandelion API?

Identify mentions of places, people, brands, and events across a variety of documents and social media channels. Seamlessly obtain additional details about these entities. Organize multilingual content into pre-established categories or develop a custom classification framework in a matter of minutes. Evaluate the sentiment expressed in short texts, like product reviews, determining if it is positive, negative, or neutral. Automatically detect important, contextually relevant concepts and key phrases within articles and social media posts. Compare two texts to analyze their syntactic and semantic similarity. Ascertain when two pieces of text relate to the same subject matter. Extract refined textual content from sources such as newspapers and blogs, removing extraneous material and advertisements to present the complete article along with its accompanying images. This method not only improves the readability of the extracted text but also highlights the most critical information, making it easier for users to grasp essential insights. By streamlining this process, users can focus more on content analysis rather than sifting through irrelevant clutter.

Media

Media

Integrations Supported

OpenRefine
Pipedream
Unremot
Virtuoso
WordPress

Integrations Supported

OpenRefine
Pipedream
Unremot
Virtuoso
WordPress

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$49 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Knowlesys Software

Company Website

www.knowlesys.com

Company Facts

Organization Name

SpazioDati

Date Founded

2007

Company Location

Italy

Company Website

dandelion.eu/

Categories and Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Categories and Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Text Mining

Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering

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