Category Archives: Sentiment Analysis

Posts about Sentiment Analysis

A tailored sentiment analysis (recorded webinar)

Last May 4th we presented our webinar “An entirely tailored sentiment analysis using MeaningCloud”. Thank you all for your attendance.

After a brief introduction to MeaningCloud and the operation of its add-in for Excel, we developed a practical example of sentiment analysis in a specific domain (restaurant reviews) and showed how MeaningCloud’s customization tools can be used to improve the accuracy of the analysis:

  • By including attributes that are relevant to the domain and focusing the analysis around them, through the creation of personal dictionaries of entities and concepts.
  • By specifying the polarity of expressions in the domain depending on the context, thanks to the definition of personal sentiment models.

Together, these tools enable our users to be greatly autonomous in the customization of MeaningCloud and put the highest-quality sentiment analysis at everybody’s fingertips.

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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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A sentiment analysis entirely tailored to your needs with our new customization tool

The adaptation to the domain is what makes the difference between a good sentiment analysis and an exceptional one. Until now, the possibilities of adapting MeaningCloud’s sentiment analysis to your domain relied on the use of personal dictionaries – to create new entities and concepts that the Sentiment Analysis API employed to carry out its aspect-based analysis – or you had to ask our Professional Services Department to develop a tailor-made sentiment model.

Sentiment Models buttonWith the release of Sentiment Analysis 2.1, we incorporated a new customization tool designed to facilitate the creation of personal sentiment models. This tool fully employs our Natural Language Processing technology to enable you to be autonomous and develop —without programming— powerful sentiment analysis engines tailored to your needs.

Other tools for customizing sentiment analysis available on the market, mostly permit to define “bags of words” with either positive or negative polarity. Our tools go far beyond and enable you to:

  • Define the role of a word as a polarity vector (container, negator, modifier), allowing to use lemmas to easily incorporate all the possible variants of each word
  • Specify particular cases of a word’s polarity, depending on the context in which it appears or its morphosyntactic function in each case
  • Define multiword expressions as priority elements in the evaluation of polarity
  • Manage how these custom polarity models complement or replace the general dictionaries of every language.

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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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Voice of the Customer in the insurance industry

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer.  By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).  

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

 

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MeaningCloud sentiment analysis powers SocialBro’s Twitter platform

The leading social marketing tool vendor applies MeaningCloud’s advanced sentiment analysis to detect the opinion of Twitter users with the highest quality and without having to develop language processing technology.

UPDATE: as of March 2016 SocialBro has been rebranded as Audiense.

SocialBro analyzes over 15 million tweets per month to extract insights that are essential for its clients’ marketing activities and campaigns. And a key ingredient of these insights is the analysis of Twitter users’ sentiment.SocialBro logo

Due to the characteristics of its business, SocialBro had some very demanding requirements in the field of sentiment analysis: a high throughput, great accuracy and the possibility of carrying out aspect-based analyses. Instead of developing its own sentiment analysis technology, SocialBro decided to turn to a specialized supplier to avoid undertaking developments outside its core business. With this aim, they chose MeaningCloud.

MeaningCloud’s Sentiment Analysis API service stands out for its semantic approaches based on advanced natural language processing. It internally employs a syntactic-semantic tree representation of the text on which it deploys the polarity of the different terms. Then, it combines and spreads these polarities according to the morphological category of each term and the syntactic relations among them.

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An Introduction to Sentiment Analysis (Opinion Mining)

In the last decade, sentiment analysis (SA), also known as opinion mining, has attracted an increasing interest. It is a hard challenge for language technologies, and achieving good results is much more difficult than some people think. The task of automatically classifying a text written in a natural language into a positive or negative feeling, opinion or subjectivity (Pang and Lee, 2008), is sometimes so complicated that even different human annotators disagree on the classification to be assigned to a given text. Personal interpretation by an individual is different from others, and this is also affected by cultural factors and each person’s experience. And the shorter the text, and the worse written, the more difficult the task becomes, as in the case of messages on social networks like Twitter or Facebook.

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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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See you at the Sentiment Analysis Symposium 2015 in New York

Next July 15-16, New York will host a new edition of the Sentiment Analysis Symposium. This event is your opportunity to keep up with technologies and solutions that help you discover business value in opinions, emotions, and attitudes in social media, news, and enterprise feedback, to further your business goals.

This year, the program has two tracks: a Presentation Track featuring a mix of business and technical presentations and panels and a Workshop Track with longer-form content. See the agenda here.

Sentiment Analysis Symposium 2015
At MeaningCloud,  over the past year we’ve seen an explosion of interest in sentiment analysis from very diverse industries and the Sentiment Analysis Symposium is the premier event to learn about the latest developments in this and related areas, such as social listening and voice of customer analytics. This is the reason why we are sponsoring the Symposium again in 2015.

We’re thrilled to present and collaborate with other leaders in the industry at this year’s event in New York. Our main presentation will be titled: “From Strangers to Acquaintances: Multidimensional Customer Profiling” and will describe how businesses aim to integrate multichannel interactions (social conversations, web behavior, contact center activity) and other data for profiling and segmenting their users in real time. In this context, a winning approach is to combine dimensions like demographics, lifestyle, brand affinity, or intent to better understand your audience and to generate business opportunities.

For more information and registering, please visit the Symposium’s website. And if you want to save 20% in your registration, contact us at news@meaningcloud.com.

Meet us in New York City, at the Sentiment Analysis Symposium, and follow @SentimentSymp.


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.
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