Sentiment Analysis has several different applications, depending on the Business. Today we’re discussing the application of NLP in machine learning to analyse customer reviews and understand how machines understand how humans communicate.
In the last year, we have seen extensive use of machine learning in our daily lives on websites such as Amazon and TripAdvisor, where it provides the user with a gist of what’s to expect from the review, saving them time and enabling them with the information to make the purchase decision.
You’ve seen this:
Text/ Doc level Sentiment Analysis can be used for gauging the overall positive or negative sentiment, or have a numeric value score of the sentiment. Although this method has it’s advantages to get an overall idea of the message, it can derive inaccurate inferences based on the composition and the choice of words used, and would not be able to detect, say, sarcasm or irony.
Sentence Level Sentiment Analysis: In which each sentence is analysed. This is the recommended method of analyzing lengthy documents, as it provides us with accurate mapping of the sentiment, per sentence. For example, one may have said good things in the first part of the document, and moved on to state the negative and end the document with positive. We will be more likely to identify the parts of the product that they liked, as well as the ones that didn’t, giving us the data for areas of improvement.
This would not be possible with a document level analysis, which would most likely give a positive to neutral result in such a case.
The system identifies the sentiment from the data, be it positive or negative as well as the topis related to these sentiments. For example, one may have stated that “ I like the bass for a pair of headphones but the sizing is not suitable as it keeps slipping off the head” In this sentence, there are two topics with different sentiments, which will both be detected by the system.
For Aspect based Sentiment Analysis the analysis is done syntactically for each sentence and extract each phrase’s functions from this analysis. For example, we know that when the verb “to like” has a phrase with the Direct Object function, this is the topic of the affinity expressed by “like”.
All in all, machine learning, is a powerful tool for success in e-commerce that effectively summarizes the sentiment of your product or service from the data, by providing the relevant information in a crisp and easy to consume graphical format.