What is it and how does it work?

Sentiment Analysis, also known as emotion AI, is a field that enables systems to determine and extract the opinions behind any textual or verbal sentences. With the internet and social media being widely used in today’s world, companies use this technology to study the market trends and opinions to design their future decisions and strategies. In addition to this, Sentiment Analysis also extracts the attributes of the expression, i.e., polarity, subject, opinion holder.

We have reached an age where we generate a massive amount of data on a daily basis and Sentiment Analysis is a tool helps us to make sense out of it. Large MNCs use this technology to develop their business techniques for better decisions and thus, higher profits. However, how do we implement such a tool? How does this work? Broadly, there are three methods to classify the techniques and algorithms used in Sentiment Analysis:

1. Rule-based – Manually designed set of rules and algorithms
2. Automatic – Machine learning techniques applied to automate the process
3. Hybrid – A blend of rule-based and automatic methods of sentiment analysis
4. Rule-Based – Plenty of open source analysis tools are available online these days. TextBlob, LinguaKit, LingPipe being some of the examples. One essential feature that every such tool offers is to determine the polarity of a given set of words. This means to analyze whether the statement portrays a positive, neutral, or negative opinion (based on the rules and algorithms used). This is done with the help ‘sentiment libraries’ that contain lists of adjectives and phrases (good and bad) and rate them on the basis of some pre-defined rules. But such methods come with many limitations as it very hard to develop a highly effective algorithm.
5. Automatic – Automatic methods follow a completely different approach from rule-based methods. They rely on feeding the system with enough data so that it can learn and evolve. Nowadays, Deep Learning (a machine language) is used to train the system. All the driverless trains and cars use Deep Learning for the vehicle to differentiate between various objects and then proceed accordingly. The workflow is usually as: gathering training data → feeding into the system → training → prediction.
6. Hybrid – Hybrid Sentiment Analysis is nothing but a combination of goods of both the worlds of rule-based and automatic methods.

Despite all the efforts that we put into the machine (to analyze the sentiment), how effective is this process? One might even wonder, is it really worth the effort? The truth is it is hard for humans (sometimes) to understand the tone of some sentences and Sheldon Cooper being a perfect cliché for it. Initially, there are chances that one may not get the desired outcome, but eventually, one can get the method to achieve 70-80% accuracy.  If the technique is being employed for the first time, results will be seen quickly. Some of the use cases that will answer all these questions are as follows:

1. Image Monitoring – Various brands across the globe want to know what people think about their products. To do this, they not only focus on the number of mentions they get on various social media platforms but also keep a check on what people have to say about them exactly.
2. Social Media – Social Media holds immense power in today’s world, and it is proven time and again when pictures/videos posted by people get millions of views/shares. Sentiment Analysis can be used to see the sentiment of people over a period of time and then prioritize and strategize things for the future.
3. Workplace – Nothing is better for an employer to know the sentiments and thoughts of his/her employees. Sentiment analysis tools can prove to be a boon at workplaces to know what an employee thinks of the company/boss, his needs, change in his thoughts over a period of time, his motivation to work, etc. Accordingly, one can plan to make things as right as possible.

There is no denial of the fact that sentiment analysis is going to be the hands-on tool for the upcoming ventures and the use case and the results may be extraordinary. However, the question that still hovers over the industry is when will the change and the prevalence of the technology expected? Well, maybe 2020 or 2030.

The team at Avyuct has tried and tested the technology for few of our clients and has got a starting point! We are soon going to launch a few more blog pieces and case studies which will be much more insightful for the techies. So, stay tuned!