Sentiment analysis- what do we understand by this?
In a layman’s language, expressing what one feels about any action taken or a product/service used which can be positive, negative, and neutral. Understanding people’s emotions have become very essential for businesses today as customers are now able and willing to express their thoughts and feelings more openly than ever before.
Sentiment analysis is also known by many other names such as:
- Opinion mining
- Sentiment mining
- Subjectivity analysis
- Opinion extraction, etc.
It allows businesses to identify customer sentiments towards their products, brands, or services. Basically, it is a process of determining the emotional tone behind a series of words that are used to develop an understanding of the attitudes, opinions, and emotions expressed.
Do we really need sentiment analysis?
Around 80% of the world’s data is unstructured and unorganized. A huge volume of data in the form of surveys, articles, emails, support tickets, chats, social media conversations, etc. is created every day which is hard to analyze and understand.
Analyzing customer feedback from survey responses to social media conversations, brands are able to pay attention to their customers and customize their products/services to meet the expectations. For instance, analyzing around 4000+ reviews about your product using sentiment analysis could help you discover if the customers are happy about your product/service.
So, that’s where sentiment analysis comes into play determining what people are saying, how they are saying it, and what do they mean by it.
Sentiment Score: A scaling system that reflects the emotional depth of emotions and assigns them a particular value say from 0 to 10, including most negative to the most positive value.
The science behind sentiment analysis is based on algorithms using natural language processing (NLP) to categorize pieces of writing as positive, neutral, or negative. NLP is basically a system that is built to extract opinions from text and tell the difference between all the words, automatically.
The algorithm is designed to identify positive and negative words, such as “fantastic”, “beautiful”, “disappointing”, “terrible”, etc.
Sentiment analysis works with 3 types of algorithms which include:
- Rule-based systems that perform sentiment analysis based on a set of manually crafted rules.
- Automatic systems that rely on machine learning techniques to learn from data.
- Hybrid systems that combine both rule-based and automatic approaches.
We find numerous applications in e-commerce, marketing, advertising, politics, and research where sentiment analysis helps in gaining an edge over others. Some of the areas benefitted by sentiment analysis are:
- Brand reputation management
- Customer feedbacks
- Crisis prevention
- Better product analytics
While it’s difficult to speculate how a relatively immature system might evolve in the future, there is a general assumption that sentiment analysis needs to move beyond a one-dimensional positive to negative scale.
However, with so many easy-to-use and affordable sentiment analysis software benefits and solutions on the market, there’s no reason you shouldn’t be listening to what customers are saying and using that information to guide your business decisions.