As of yesterday our Sentiment Analysis API is now available in Portuguese, joining five other languages. This had been a fairly frequent request from our friends in Portuguese-speaking countries.
For your reference, the following table summarizes MeaningCloud’s current language coverage:
Finding committed employees is one of public and private organizations’ top priorities. Thus, listening to the Voice of the Employee by systematically collecting, managing and acting on the employee feedback on a variety of valuable topics is essential.
The relationship between Voice of the Employee (VoE) and Engagement is very similar to the one between Voice of the Customer (VoC) and Customer Experience. VoC provides information to improve customer experience. Voice of the Employee promotes employees’ engagement in the company and their work. See: Voice of the Employee, Voice of Customer and NPS
Voice of the Employee collects the needs, wishes, hopes, and preferences of the employees of a given company. VoE considers specific needs, such as salaries, career, health, and retirement, as well as implicit requirements to satisfy the employee and gain the respect of colleagues and managers.
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Last February 9th we presented our webinar “Classification, topic extraction, clustering… When to use the different Text Analytics tools?”. Thank you all for your interest.
During the session we covered the following agenda:
IMPORTANT: The data analyzed during the webinar can be found in this tutorial.
Interested? Here you have the presentation and the recording of the webinar.
(También presentamos este webinar en español. Tenéis la grabación aquí.)
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One of the questions we get most often at our helpdesk is how to apply the text analytics functionalities that MeaningCloud provides to specific scenarios.
Users know they want to incorporate text analytics into their processes but are not sure how to translate their business requirements into something they can integrate into their pipeline.
If you add the fact that each provider has a different name for the products they offer to carry out specific text analytics tasks, it becomes difficult not just to get started, but even to know exactly what you need for your scenario.
In this post, we are going to explain what our different products are used for, the NLP (Natural Language Processing) tasks they are tied to, the added value they provide, and the requirements they fulfill.
[This post was last updated in October 2018 to include our new functionalities.]
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Most valuable information for organizations is hidden in unstructured texts (documents, contact center interactions, social conversations, etc.). Text Analytics helps us to structure such data and turn it into useful information. But which text analytical tools are the most appropriate for each case? When should I use information extraction, categorization, or clustering? Which applications can benefit most from Text Analytics? What are the challenges?
Register for this MeaningCloud webinar on Wednesday, February 8th at 9:00 PDT and discover answers to these and other questions through practical examples.
UPDATE: this webinar has already taken place. See the recording here.
(Este webinar también se realizó en español, ver la grabación aquí.)
In the previous tutorial we published about Text Classification and MeaningCloud’s Excel add-in, we showed you step by step how to carry out an automatic text classification using an example spreadsheet.
In this tutorial, we are going a bit further: instead of just using one of the predefined classification models we provide, we are going to create our own model using the model customization console in order to classify according to whichever categories we want.
We are going to work with the same example as before: London restaurants reviews extracted from Yelp. We will use some data from the previous tutorial, but for this one we need more texts, so we’ve added some. You can download the spreadsheet here if you want to follow the tutorial along.
If you followed the previous tutorial, you might remember that we tried to use the IAB model (a predefined model for contextual advertisement) to classify the different restaurant reviews and find out what type of restaurants they were. We had limited success: we did obtain a restaurant type for some of them, but for the rest we just got a general category, “Food & Drink“, which didn’t tell us anything new.
This is where our customization tools come in. Our classification models customization console allows you to create a model with the categories you want and lets you define exactly the criteria to use in the classification.
So how do we create this user model?
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Last October 5th we presented our webinar “Learn to develop custom text classifiers with MeaningCloud”. Thank you all for your attendance.
We began by presenting how to do text classification with MeaningCloud and why it is necessary to develop models that are adapted to each specific application scenario. The bulk of the presentation consisted in using a practical case (analysis of restaurant reviews) to show how these models can be developed using our product.
As you probably already know, Excel spreadsheets are one of the most extended ways of working with big collections of data. They are powerful and easy to combine and integrate with a myriad of other tools. Through our Excel Add-in, we enable you to add MeaningCloud’s analysis capabilities to your work pipeline. The process is very simple as you do not need to write any code.
In this tutorial, we are going to show you how to use our Excel Add-in to perform text classification. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp. If you have already read some of our previous tutorials, this first part may sound familiar.
To get started, you need to register in MeaningCloud (if you haven’t already), and download and install the Excel add-in on your computer. Here you can read a detailed step by step guide to the process.
Once you’ve installed it, a new tab called MeaningCloud will appear when you open Excel. If you click on it, you will see the following buttons:
To start using the add-in, you need to copy your license key and paste it into the corresponding field in the Settings menu. You are required to do this only the first time you use the add-in, so if you have already used it, you can skip this step.
Once the license key is saved, you are ready to start analyzing!
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Users frequently ask us through our support line how to perform text classification according to application-specific taxonomies. For example, somebody needing to analyzing a bank’s contact center calls and open survey responses might be interested in classifying such messages according to the institution’s different types of products and services (deposits, loans, mortgages, etc.) or the type of interaction (request for information, contracting, complaint, etc.).
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