Category Archives: Sentiment Analysis

Posts about Sentiment Analysis

Use case: VoC program for retail

Voice of the Customer (VoC) programs have become an established path for retailers to deliver enhanced customer experiences.

Consumer behavior, nevertheless, is always changing. Retailers are rarely able to anticipate these behavioral changes or adapt quickly enough to preserve or grow their market share.

In 2018, a regional supermarket brand with over 800 hundred stores wanted to understand customer experience at every touchpoint in order to identify potential areas of customer frustration.

The company undertook a strategic Voice of the Customer (VoC) program with the aim of systematically and consistently capturing insights from the customer experience.

The program is still running. It comprises of around 23,000 surveys per month, completed by customers at various branches of the supermarket chain.

In retail, listening to the Voice of the Customer to identify the strengths and weaknesses of business is fundamental. Competition is fierce. Given that the scale of information to be analyzed is immense, the company decided to work with MeaningCloud to process the literal answers to the open-ended questions of the surveys, so they need not worry about the amount or the time needed to process them.
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Invoking the MeaningCloud Sentiment Analysis API from Minsait’s Onesait Platform

Minsait’s Onesait Platform is an IoT & Big Data Platform designed to facilitate and accelerate the construction of new systems and digital solutions and thus achieve the transformation and disruption of business. Minsait is a brand of Indra: its business unit addressing the challenges posed by digital transformation to companies and institutions.

Minsait has published a post about the procedure to invoke an external API from the integrated flow engine of the Onesait Platform (formerly known as Sofia2).

MeaningCloud integrated with Minsait Onesait Platform

The post titled HOW TO INVOKE AN EXTERNAL REST API FROM THE SOFIA2 FLOW ENGINE? uses as an example the integration of MeaningCloud Sentiment Analysis API (in Spanish).

The article illustrates one of the strengths of MeaningCloud: how easy it is to integrate its APIs into any system or process.

MeaningCloud Release: Sentiment + Nordic Pack

Not long ago we published the first of our Language Packs: the Nordic pack, which includes several text analytics tasks in Swedish, Danish, Norwegian and Finnish.

Among the text analytics tasks supported, there’s one that was missed by many of you: Sentiment Analysis API. Well, no more!

We are happy to announce that from now on you can also analyze sentiment in the four languages included in the Nordic pack. And what’s more, for those of you that are already subscribed to the pack, it has been automatically included and so you can start using it right away without any change in pricing.

MeaningCloud release

For those of you that are not subscribed to the Nordic pack, remember that you can test all our packs full functionality by requesting a 30 day period trial. It’s super easy!

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Voice of the Employee Dashboard

Voice of the Employee gathers the needs, wishes, hopes, and preferences of all employees within an organization. The VoE takes into account both explicit needs, such as salaries, career, health, and retirement, as well as tacit needs such as job satisfaction and the respect of co-workers and supervisors. This post follows the line of Voice of the Customer in Excel: creating a dashboard. We are creating another dashboard, this time for the Voice of the Employee.

Text-based data sources are a key factor for any organization that wants to understand the “whys”.

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Meaningful Voice of the Patient at ePharma 2018 – New York

MeaningCloud sponsors the E-pharma 2018 which is taking place in New York, March 21-23. MeaningCloud’s value proposition for the conference can be summarized as AI-based Voice of the Patient analysis for Patient Experience.

ePharma 2018 is about patient-centricity and engaging with patients with digital marketing.For pharmaceutical companies, it is vital to understand the feedback that their customers, both current and potential, express through all types of channels and contact points.

Text Analytics technologies automatically process and analyze textual content and provide valuable insights, transforming text-based “raw” data into structured, usable information.


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Join MeaningCloud at the 2016 Sentiment Analysis Symposium

Banner Sentiment Analysis SymposiumMeaningCloud is excited to be sponsoring the 2016 Sentiment Analysis Symposium, taking place July 12 in New York. Join us there!

The Symposium is the first and best conference to address the business value of sentiment, opinion, and emotion in social, online, and enterprise data. The audience is comprised of business analysts, developers, data scientists, and researchers, applying text, sentiment, and social analytics to a host of business challenges. And the speakers? They represent users like Johnson & Johnson, the Mayo Clinic, and VML, analysts like Forrester Research, and innovative start-ups and established technology players.

We will present MeaningCloud’s text and sentiment analysis technology during the symposium program, and you can meet us for a personalized demo in the SAS16 exhibit area or for an informal chat during symposium networking breaks.

If you’re up for a deep technical introduction, start your Symposium experience with an optional half-day tutorial — Computing Sentiment, Emotion, and Personality — taught July 11.

There’s good reason the Symposium has been going strong since 2010. Come network and learn with some of the best sentiment and social data innovators around. Use the registration code MEANING to save 20% on your ticket — register online here — and we’ll see you in New York!

Sentiment Analysis in Excel: optimizing for your domain

In previous tutorials about Sentiment Analysis and our Excel add-in, we showed you step by step how to carry out a sentiment analysis with an example spreadsheet. In the first tutorial we focused in how to do the analysis, and then we took a look at the global polarity we obtained. In the second tutorial, we showed you how to customize the aspect-based sentiment analysis to detect exactly what you want in a text through the use of user dictionaries.

In this tutorial we are going to show you how to adapt the sentiment analysis to your own subdomain using of our brand new sentiment model customization functionality.

We are going to continue to use the same example as in the previous tutorials, as well as refer to some of the concepts we explain there, so we recommend to check them out beforehand, specially if you are new to our Excel add-in. You can download here the Excel spreadsheet with the data we are going to use.

The data we have been working on are restaurant reviews extracted from Yelp, more specifically reviews on Japanese restaurants in London.

In the last tutorial, we saw that some of the results we obtained could be improved. The issue in these cases was that certain expressions do not have the same polarity when we are talking about food or a restaurant than when we are using them in a general context. A clear example of this is the verb ‘share’. It is generally considered something positive, but in restaurant reviews it’s mostly mentioned when people order food to share, which has little to do with the sentiment expressed in the review.

This is where the sentiment model customization functionality helps us: it allows us to add our own criteria to the sentiment analysis.

Let’s see how to do this!
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