Sentiment analysis is more precise than ever before. That’s thanks to rapid developments in the field of natural language processing (NLP) over the last few years. Now more than ever, you can get accurate and meaningful insights from online conversations.
But what exactly is sentiment analysis? How do you define sentiment and how can you combine sentiment analysis with other analytics to get deeper insights for your business?
Consider this blog your definitive guide to all things “sentiment”.
Sentiment can be generally defined as the attitude, opinion or feeling of a person towards something. This could be an event, another person, an object, etc.
In the context of sentiment analysis, we’re interested in understanding the sentiment of people towards a particular topic. This is also known as “opinion mining”. Using AI, we can automatically detect sentiment values from written text wherever it occurs, be it on social media, on a review site, in a chat transcript or in a news report.
Sentiment in opinion mining is measured as either positive, negative, or neutral.
The Difference Between Sentiment and Emotion
Sentiment is distinct from emotionality, but the two concepts are often confused. Emotionality is the degree to which a person experiences emotions while giving their opinion.
It is quite possible to have a positive or negative opinion about something while being emotionally neutral. Most people can think of an example of a type of food that they don’t enjoy, but their dislike doesn’t make them upset or disgusted.
On the other hand, it’s also possible to have an emotionally charged opinion about something without it being positive or negative. For example, come the FIFA World Cup later this year, many people will feel passionately about the fact that they are indifferent to football.
Emotion analysis is the task of detecting and classifying the emotions expressed in text, which is different from sentiment analysis.
This table illustrates the distinction between sentiment and emotion:
Anger, Disgust, Fear, Sadness
Now that we’ve clarified the sentiment definition, let’s move on to how sentiment analysis works.
As we mentioned, sentiment analysis is the process of automatically understanding whether the sentiment in a text is positive, negative or neutral.
For sentiment analysis, NLP algorithms analyse text data to identify and extract subjective information. Sentiment analysis accuracy depends on the quality of the training data that’s used.
Broadly speaking, sentiment analysis can be performed using two different approaches: rule-based and statistical, or a combination of the two.
Rule-based sentiment analysis
Rules-based sentiment analysis is the most straightforward of the two methods. The rules-based approach uses a lexical database compiled by language experts. With this approach, sentences are broken down into parts of speech and words are assigned a sentiment – either positive, neutral or negative.
Rules-based models are time-consuming to build, and they can’t adapt and evolve with changes in language, for example, informal language, that can change rapidly over time and across different regions. In this respect they’re very limited.
Statistical sentiment analysis
Statistical (or machine learning-based) sentiment analysis, on the other hand, is a data-driven approach that builds models to identify sentiment.
In this approach, developers train the computer by feeding it examples and assigning them meaning. The computer then deciphers different patterns and map it to a concept such as semantics or intent.
Supervised and semi-supervised models (which use some labelled data for their classification models) improve classification accuracy. But unsupervised models are getting more sophisticated.
While this approach requires initial training from a developer, it ultimately learns by itself. Unlike the rules-based approach, statistical sentiment analysis can adapt to new conditions. It can also infer meaning from context, and compensates for errors in grammar and syntax, improving its overall accuracy.
The future of machine-learning based sentiment analysis is increasingly accurate, and increasingly the preferred option as advanced models become more accessible.
Combining Sentiment with Other Customer Analytics
Emotion analysis enables you to get more specific insights into exactly how your audience feels about different elements of your business. High emotionality is also an indicator for customer loyalty. Those who express strong positive sentiment and emotions are likely to remain loyal to your business. You can use this information to identify key influencers – those with a strong positive emotional connection to your brand and a high social capital can help spread the word about your business.
Conversely, those who express strong negative sentiment and emotions are most at risk of churning. Use emotion analysis to alert your customer to at-risk customers so they can take action to save the customer relationship.
HR teams can also use emotion and sentiment analytics to measure employee satisfaction and intervene before sentiment deteriorates to the point of affecting employee retention.
Psychographics is the study of personality, attitudes, values, interests and lifestyles. When sentiment is combined with psychographic analysis, businesses can get a more complete understanding of their customers. For example, you can discover how the various segments of your audience respond differently to your offerings or marketing efforts.
Sentiment analysis is a valuable tool for customer analytics, but it’s only one piece of the puzzle. To get the most insights into your audience, combine sentiment with other types of customer data such as emotion and psychographics. Doing so will give you a well-rounded view of your customers that can be used to improve the customer experience.
Get Started With Symanto
At Symanto, we’re at the forefront of NLP research and technology. We’re constantly exploring new ways to help businesses extract insights from their data. Our tools for sentiment and emotion and detection, as well as our unique psychographics tools are just some of the ways we can help you get more out of your customer data.