Blog #10

Blog #9

 4 Sep 2017

In my last post we introduced the Bayes rule and it's relationship with sentiment analysis. In this post we'll talk about some of the difficulties of applying sentiment analysis and what we can do to try and improve the accuracy.

Sentiment analysis can be applied to many areas but arriving at whether a statement is positive or negative can be difficult. The categorisation is mainly split into two type’s facts and opinion.

Facts are expressed about entities, whereas events are about their properties.

Lui discusses that opinions are completely subjective and describe people’s sentiments, appraisals or general feeling towards entities and their properties. (Lui, 2010).

The human language can be complex for machine based learning systems to interpret and opinions can be expressed with sarcasm or irony.  The order of words for can add even more confusion.

Take the following example  (Frank):

“I currently use the Nikon D90 and love it, but not as much as the Canon 40D/50D. I chose the D90 for the video feature. My mistake.”

In this example, the author is conveying sarcasm; this can be hard for classifiers to process (Frank)

“After a whole 5 hours away from work, I get to go back again, I'm so lucky!”

For a classifier to process data and provide more accurate results it must be trained.  This can be achieved by collecting training data.  Various sources can be used, one popular means is to use a corpus of move reviews labelled as positive or negative.  The algorithm is then applied - the best accuracy for this approach is approximately 82.9% (Read)

 

If you'd like to know more about Sentiment Analysis, it's difficulties and solutions, please click the image below to Download our free Sentiment Analysis Whitepaper.

Or View Our Diary And Book In A Call

Also included in the Whitepaper:

  • An Introduction To Sentiment Analysis
  • Bayesian Theorem And It's Connection To Sentiment Analysis
  • How To Train Your Classifier When Performing Sentiment Analysis
  • What Is POS Tagging And How You Can Use It

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