Author Archives: Blanca Galego

Text Classification in Excel: build your own model

Customized Text Classification for Excel

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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Text Classification in Excel: getting started

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:

excel add-in ribbon

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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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!
Continue reading →

Sentiment Analysis 2.1: Migration guide

We have released a new version of our sentiment analysis API, Sentiment Analysis. In Sentiment Analysis 2.1:

  • We’ve changed how the sentiment model is sent in order to enable the use of custom sentiment models across all the APIs that support sentiment analysis.
  • Support to analyze documents and URLs has been added.
  • A configurable interface language has been added to improve multilingual analyses.

As you would see, this is a minor version upgrade, so the migration process will be fast and painless. In this post, we explain what you need to know to migrate your applications from Sentiment Analysis 2.0 to Sentiment Analysis 2.1.
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Lemmatization, PoS, Parsing 2.0: Migration guide

We have released a new version of our core linguistic analyzer: Lemmatization, PoS and Parsing. In Lemmatization, PoS and Parsing 2.0:

  • More analysis possibilities have been included to allow you to combine a complete morphosyntactic analysis with other types of analysis such as Sentiment Analysis and Topics Extraction.
  • Configuration options have been changed to provide more flexibility in the analyses and to make the options available more understandable.
  • We’ve refactored our code to:
    • Improve the quality of the concepts/keywords extraction.
    • Make easier and more flexible the use and traceability of user dictionaries.
    • Give the possibility of obtaining a more complex integrated analysis to give flexibility in complex scenarios where the standard output is not enough.
  • A new type of topic has been added, quantity expressions, to cover a specific type of information that was hard to obtain with previous versions.
  • Some fields in the output have been modified, either to give them more appropriate names or to make them easier to use and understand.
  • Some use modes have been retired as the information provided was redundant with what a morphosyntactic analysis already gives.

All these improvements mean the migration process is not as fast as it would be with a minor version. These are the things you need to know to migrate your applications from Lemmatization, PoS and Parsing 1.2 to Lemmatization, PoS and Parsing 2.0.
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Topics Extraction 2.0: Migration guide

We have released a new version of our information extraction API, Topics Extraction. In Topics Extraction 2.0:

  • The topics extracted have been reordered to extract information in a more coherent way.
  • Configuration options have been changed to provide flexibility in the analyses and to make the options available more understandable.
  • We’ve refactored our code with two short-term goals in mind:
    • Improving the quality concepts/keywords extraction.
    • Making easier and more flexible the use of user dictionaries.
  • A new element has been added, quantity expressions to cover a specific type of information that was hard to obtain with previous versions.
  • Some fields at the output have been modified, either to give them more appropriate names or to make them easier to use and understand.
  • A configurable interface language has been added to improve multilingual analyses.

All these improvements mean the migration process is not as fast as it would be with a minor version. In this post, we explain what you need to know to migrate your applications from Topics Extraction 1.2 to Topics Extraction 2.0.
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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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Sentiment Analysis 2.0: Migration guide

We have released a new version of one of our more popular APIs: Sentiment Analysis. In Sentiment Analysis 2.0:

  • The rules used for defining polarity terms have been greatly improved, adding new operators and making the models used much more flexible, which in turn leads to better results.
  • Sentiment analysis is now done at more levels, allowing to identify more complex syntactic structures and to obtain more detailed information about how the polarity is expressed.
  • More configuration options have been added related to the morphosyntactic analysis over which the sentiment analysis is carried out.
  • The architecture of the service has changed, leading to a tenfold improvement in the response time.
  • An integration with the Lemmatization, PoS and Parsing API has been added in order to ease the way of creating applications that use the information provided by both APIs.
  • Dictionary customization has been fully integrated in order to get out the most out of its functionality.

All these improvements mean the migration process is not as fast as it would be with a minor version. These are the things you need to know to migrate your applications from Sentiment Analysis 1.2 to Sentiment Analysis 2.0.
Continue reading →