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.

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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Heads up!

This tutorial was made for Textalytics and as such, it has become obsolete. You can read the updated version for MeaningCloud in this post.

MeaningCloud provides an API to carry out advanced opinion mining, Sentiment Analysis, which extracts both a global aggregated polarity of the text and a more in-depth analysis, giving a sentence-level breakdown of the polarity, extracting entities and concepts and the sentiment associated to each one of them.

Marvel’s Black Widow #1

What makes MeaningCloud Sentiment Analysis API different is the possibility of defining entities and concepts for each call of the API, allowing you to obtain the same detailed sentiment analysis for entities or concepts specific to the domain of your application.

We are going to use comic book reviews to learn how to use this feature, as it’s a very rich domain in which it’s easy to illustrate how useful user-defined concepts and entities can be. This applies either to this field or to others where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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