Sentiment Analysis, Customer Feedback, New ML Algorithms

Sentiment Analysis, Customer Feedback, New ML Algorithms

When using an online chatbot in the context of customer service, the chatbot in question needs to understand the various forms of feedback that customers provide to the business that has implemented the chatbot. To this point, sentiment analysis, also known as opinion mining, is a Natural Language Processing technique that is used to determine whether a particular input or piece of data is positive, neutral, or negative, particularly as it relates to the understanding of customer needs and feedback. To provide an example of this, when consumers choose to end their subscription for a particular product or service, they will often be asked the specific reason that led to their decision. Through the use of sentiment analysis, businesses can gauge customer feedback in a more efficient manner. Moreover,  this feedback can then be used to improve their products and services.

How does sentiment analysis work?

Sentiment analysis works in conjunction with the implementation of machine learning algorithms that are used to assign a score to a data set in accordance with positive, negative, and neutral feedback. As is the case with any application of Natural Language Processing, a software developer looking to create a sentiment analysis algorithm would first start by creating a language model that would account for any feedback that a potential customer would provide to the said algorithm. For instance, a sentiment analysis algorithm that was designed for use on a website that is used to sell clothing would be trained upon sample utterances and inputs concerning prices, tracking labels, specific types of clothing, a return policy, etc. As such, sentiment analysis can be achieved using a number of different methods and techniques.

Generally speaking, sentiment analysis algorithms will fall into three distinct categories, rule-based, automatic, and hybrid systems. These three categories are very similar to supervised, unsupervised, and semi-supervised learning algorithms. In a ruled-based sentiment analysis algorithm, the software developer will create specific rules that govern the functionality of the feedback system. On the other hand, an automatic sentiment analysis system will instead rely on machine learning techniques to function. Finally, a hybrid sentiment analysis algorithm will combine the most beneficial features of both rule-based and automatic sentiment analysis algorithms. With this being said, there are various types of sentiment analysis.

Graded sentiment analysis

As the name suggests, graded sentiment analysis functions on the basis of interpreting customer sentiment or feedback according to a particular grading system. A common example of grading sentiment analysis are five-star rating systems, in which consumers convey their feedback in accordance with a particular number of stars. In such grading systems, a grade of 5 stars is representative of positive feedback, 3 stars are representative of neutral feedback, and 1 star is representative of negative feedback. Alternatively, a sentiment analysis system could also implement a grading system that functions on the basis of varying degrees of positive and negative feedback. Such a system would include responses that are very positive, positive, neutral, negative, and very negative.

Emotion detection

Conversely, emotion detection is another method that software engineers can use to create a sentiment analysis system. Emotion detection sentiment analysis allows customers to provide feedback to businesses in accordance with specific emotional responses. Liking a post on a social media website such as Instagram or Twitter is perhaps the best example of such a system, as users can communicate their feedback by giving a particular photo or image a thumbs up or thumbs down. This approach has also proven to be extremely popular on the video streaming and social media platform Youtube, as popular personalities on the website are able to monetize their content by garnering positive feedback in the form of likes and views on their respective videos.

Aspect-based sentiment analysis

A third technique that can be used to create sentiment analysis systems is aspect-based sentiment analysis. While emotional detection and graded sentiment analysis can be used to gauge customer feedback in a quick and effective way, such responses are inherently vague. As such, aspect-based sentiment analysis instead functions on the basis of the more specific aspects of the feedback that customers provide to businesses. A common example of aspect-based sentiment analysis are online reviews for a particular product or service. For example, customers that purchase a computer may leave comments on the manufacturer’s website remarking that the computer does not have strong battery life. As such, an aspect based-classifier would be able to determine that such a sentence represents negative feedback concerning the product in question.

As machine learning has enabled software engineers to create cutting-edge and innovative new products, it has also provided customers with new ways to provide feedback concerning the quality of said products. While direct feedback through the means of written or verbal communication will always be the most effective way for human beings to express their feelings or ideas as it relates to a particular topic or issue, sentiment analysis provides customers with the opportunity to convey their opinions in a variety of different ways. As such, while many consumers may not be aware of sentiment analysis, they have undoubtedly expressed their emotions regarding a particular product or service when shopping online.