Tag Archives: rapidminer

Posts related to RapidMiner

RapidMiner + Python + MeaningCloud = 🚀

Integrations with third-party software are something extremely useful: they allow you to use technology outside the tool you are using, giving you additional features outside its core functionality or just providing auxiliary tools to make your day to day easier.

One of the downsides is that you are limited by the functionality the integration provides. Usually, this is not much of a problem as standard integrations tend to cover the most common use cases, but in the case of tools that can be used in many scenarios, these uses cases may not be exactly what you need or want for your application.

MeaningCloud is not an exception to this. We provide many different APIs, each one of them with several types of analyses and with tons of possible applications. It’s not surprising that not all of them are included in MeaningCloud’s extension for RapidMiner.

mc+python+rm

If you want something like the global polarity Sentiment Analysis provides, then the extension for RapidMiner has you covered, but it may not be the case for other analyses. It can go from wanting to use a MeaningCloud API not included in the extension such as the Summarization API or to something as small as needing the label of the resulting categories in an automatic classification process instead of the code the extension provides.

Last year, RapidMiner published a new Python scripting extension: Execute Python. This operator allows you to run a Python script in RapidMiner, which enables you to include any processing you want and can code in a Python script in your RapidMiner process.

Using this new functionality and MeaningCloud’s Python SDK, we can create a Python script to use any of MeaningCloud APIs directly from RapidMiner. The SDK enables us to work with the API output easily and to extract whatever information we want to add to our RapidMiner processes.

Let’s see how we can do this! Continue reading


RapidMiner: Impact of topics on the sentiment of textual product reviews

This is the second of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. Read the first one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this RapidMiner tutorial we shall attempt to extract a rule set that will predict the positivity/negativity of a review based on MeaningCloud’s topics extraction feature as well as sentiment analysis.

To be more specific, we will try to give an answer to the following question:

  • Which topics have the most impact in a customer review and how do they affect the sentiment of the review that the user has provided?

For this purpose, we will use a dataset of food reviews that comes from Amazon. The dataset can be found here.

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RapidMiner: Relationship between product scores and text review sentiment

This is the first of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. See the second one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this tutorial we shall analyze a set of food reviews from Amazon. We will use the MeaningCloud sentiment API and try to see how users score products and whether their review description of a certain product corresponds to the score that they have assigned – more specifically we will try to see

  • How closely the review sentiment corresponds to the manually assigned score (which we already have available in our dataset).

The dataset that we will be using throughout the tutorial can be found here. First thing we need to do is download the CSV to our computer.

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You can now use MeaningCloud with RapidMiner

Expand text analytics with the tools to create the most sophisticated predictive models

At MeaningCloud, we have just launched a feature that enables users to incorporate our text analytics into complex predictive models based on structured data. With our new Extension for RapidMiner you can directly embed our semantic analysis engines into the process pipelines defined in this popular analytical tool.

RapidMiner is an open-source platform for data science, recognized as a leader in the field of advanced analytics tools. RapidMiner is used for preparing data, creating predictive models, validating them, and embedding them into business processes quickly and easily .

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