Category Archives: Tutorials

Posts about Meaningcloud’s tutorials.

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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Sentiment Analysis in Excel: customizing aspect-based analyses

In the previous tutorial we published about Sentiment Analysis and MeaningCloud’s Excel add-in, we showed you step by step how to do a sentiment analysis using an example spreadsheet. Then we showed you a possible analysis you could obtain with its global polarity results.

In this tutorial we are going a bit further: instead of analyzing the global polarity obtained for different texts, we are going to focus on the analysis of different aspects that appear in them and how to use our dictionaries customization console to improve them and to extract easily the exact information you are interested in.

We are going to work withe same example as before: reviews for Japanese restaurants in London extracted from Yelp. If you don’t have it already from the previous tutorial, you can download the spreadsheet with the data here.

If you followed the previous tutorial, you will remember that when you run the sentiment analysis without changing its default settings, two new sheets appear: Global Sentiment Analysis and Topics Sentiment Analysis. Topics Sentiment Analysis shows you the concepts and entities detected in each one of the texts and the sentiment analysis associated to each one of them.

But what can we do when these are not the aspects of the text we are interested in analyzing? This is where our customization tools come in. Our dictionaries customization console allows you to create a dictionary with any of the concepts or entities you want to detect in your analysis, down the type you want them to have associated.

So how do we create this user dictionary?
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Sentiment Analysis in Excel: getting started

Excel spreadsheets are one of the most extended ways of working with big collections of data. They are very powerful and they are very easy to combine and integrate with a myriad of other tools. Through our Excel Add-in we provide you a way of adding MeaningCloud’s analyses to your work pipeline. It’s very simple and it has the added benefit of not needing to write any code to do it.

In this tutorial we are going to show you how to use our Excel Add-in to do sentiment analysis. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp.

To get started, first you need to register in MeaningCloud (if you haven’t already), and download and install the Excel add-in in your computer. Here you can read a detailed step by step of the process.

Once you’ve installed it, a new tab called MeaningCloud should appear when you open Excel. If you click on it, these are the buttons you will see.

excel add-in ribbon

The first thing you need to do to start using the add-in is to copy your license key and paste it on the corresponding field in the settings menu. You will only have to do this 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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#ILovePolitics: Political discourse analysis in social media

We continue with the #ILovePolitics series of tutorials! We will show how to use MeaningCloud for extracting interesting insights to build your own Political Intel Reports and, at the same price, turning you into a Data Scientist giant in the field of Social Media Analytics.

political issues

Political issues

Politics and Social Media Analytics

Our research objective is to study and compare the discourse of different politicians during the electoral campaign, using their messages in Twitter. We are going to compare tweets by the four most popular (mentioned) politicians in our previous tutorial: Barack Obama (@barackobama), Hillary Clinton (@HillaryClinton), Donald Trump (@realDonaldTrump) and Jeb Bush (@JebBush).

  • What are their key messages?
  • What do they focus on?
  • Are really there different ways of doing politics?

Before we start, three remarks: 1) we will focus on U.S. Politics, in English language, but the same analysis can be adapted for your own country or language as long as it is supported in MeaningCloud, 2) this is a technical tutorial: we will develop some coding, but in general, everyone can understand the purpose of this tutorial, and 3) although this tutorial will use PHP, any non-rookie programmer can translate the programs to any language.

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#ILovePolitics: Popularity analysis in the news

If you love politics, regardless of your party or political orientation, you may know that election periods are exciting moments and having good information is a must to increase the fun. This is why you follow the news, watch or listen to political analysis programs on TV or radio, read surveys or compare different points of view from one or the other side.

American politics in a nutshell

American politics

Starting with this, we are publishing a series of tutorials where we will show how to use MeaningCloud for extracting interesting political insights to build your own political intel reports. MeaningCloud provides useful capabilities for extracting meaning from multilingual content in a simple and efficient way. Combining API calls with open source libraries in your favorite programming language is so easy and powerful at the same time that will awaken for sure the Political Data Scientist hidden inside of you. Be warned!

Our research objective is to analyze mentions to people, places, or entities in general in the Politics section of different news media. We will try to carry out an analysis that can answer the following questions:

  • Which are the most popular names?
  • Does their popularity depend on the political orientation of the newspaper?
  • Is it correlated somehow to the popularity surveys or voting intentions polls?
  • Do these trends change over time?

Before we begin

This is a technical tutorial in which we will develop some coding. However, we will try to guide you through the whole process, so everyone can follow the explanations and understand the purpose of the tutorial.

For the sake of generality and better understanding, we will focus on U.S. Politics in English, but obviously you can easily adapt the same analysis for your own country or (MeaningCloud supported) language.

And last but not least, this tutorial will use PHP as programming language for the code examples. However, any non-rookie programmer should be able to translate the scripts into any language of their choice.

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Feature-level sentiment analysis

Back when we were called Textalytics, we published a tutorial that showed how to carry out feature-level sentiment analysis for a specific domain: comic book reviews.

Cover for Marvel's Black Widow #1

Marvel’s Black Widow #1

Since then, besides changing our name, we have improved our Sentiment Analysis API and how to customize the different analyses through our customization engine. In this post we are going to show you how to do a feature-level sentiment analysis using MeaningCloud.

One of the main changes in the latest release of our API is the possibility of using custom dictionaries in the detailed sentiment analysis provided by the Sentiment Analysis API. We are going to use comic book reviews to illustrate how to work, but the same process applies to any other fields where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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