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Sentiment Analysis

What is Sentiment Analysis?


Sentiment Analysis is the process of deciding whether a piece of writing is positive, negative, or unbiased. A sentiment analysis system combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to writing within a sentence or phrase.

Sentiment analysis helps measure public opinion, conduct nuanced market research, monitor brand and product reputation, and understand customer experiences. 

It permits businesses to understand customer sentiment toward products, brands or services in online discussions and feedback.

Understanding individuals' emotions online is critical for businesses; since customers can communicate their thoughts and feelings more transparently. By automatically analyzing customer feedback, from survey responses to social networking discussions, brands can listen mindfully to their clients, and tailor products and services to meet their needs.

Sentiment score is a scaling framework that mirrors the emotional depth of emotions in a piece of content. It detects emotions and appoints them a particular value, from 0 up to 10 – from the most negative to most positive.

 

Why is Sentiment Analysis important?

Firstly, sentiment analysis saves time and effort since the process is completely automated – so human involvement is reduced.

Can you imagine browsing the Web, finding relevant content, understanding them, and gauging the tone they carry manually? That would be highly time-consuming and ineffective.

Secondly, sentiment analysis is significant on the grounds that feelings and perspectives towards a subject can become actionable pieces of information helpful in various areas of business. This information can be utilized to proactively identify and solve customer grievances. 

Thirdly, sentiment analysis is turning into an increasingly popular topic as AI, machine learning and natural language processing technologies are thriving these days.

Fourthly, with sentiment analysis, businesses will be forced to improve product quality. Market research teams would be able to gauge customer preferences better. This would also help in identifying opportunities for upselling, reduce customer churn, increase customer acquisition and retention. 

What is sentiment analysis used for?

Sentiment analysis has multiple applications in e-commerce, marketing, advertising, politics, and research. It is also extremely helpful in social media monitoring as it allows us to gain an overview of the wider public sentiment behind certain topics. 

Let’s have a closer look at how sentiment analysis benefits certain areas in business-

Social Media Monitoring

Prioritizing activity - What do you think is more important? A fuming customer or a “thank you” shoutout? The annoyed customer is the obvious choice. Sentiment analysis allows you analyze pieces of writing containing predefined keywords in real-time. 

On receiving a negative mention, a business can react immediately and nip the issue in the bud before it escalates into a social media crisis.

Keep a track on competition - Why not track your competitors’ social platforms the same way you track your own? If you look closely, you may notice there’s been negative feedback to a particular feature of their new product, and you respond by designing a campaign focusing on that gap. 

Brand Reputation Monitoring

Not only do brands have a plethora of information available on social media, but also across the internet. Instead of focusing on specific social channels like Facebook and Twitter, you can also find mentions in places like news sites, blogs, and forums – again, focusing on not just the volume, but also the quality of those mentions.

The Internet is where consumers discuss brands, products, services, share their experiences and recommendations. Social media, review sites, blogs, and discussion forums are boiling with opinions which, if collected and analyzed, are a rich source of business information.

This is how businesses can discover consumer perspectives towards their products, services, campaigns and brands expressed on consumer forums, review sites, news sites, blogs, and other publicly available online sources.

Customer feedback

Social media and brand monitoring provide us with immediate, unfiltered information on customer attitudes. However, there are two additional sources of insight – surveys and customer support interactions.

Results received from surveys can be used to design better-informed questions for future surveys, understand the shifts in customer experience over time, provide internal teams a closer look into product performance and much more.

Track your average sentiment over time to get a sense of how your customers feel about your business. Building on from here you can look at segmenting the data and comparing different segments based on various attributes.

How does Sentiment Analysis work?

Basic sentiment analysis of text follows a straightforward process:

  1. Each text document is broken down into its component parts (sentences, phrases, and parts of speech)
  2. Each sentiment-bearing phrase is identified
  3. A sentiment score is assigned to each phrase and component (-1 to +1)


The main types of algorithms used include:

  • Rule-based systems - analysis based on a set of manually crafted rules.
  • Automatic systems - analysis based on machine learning techniques.
  • Hybrid systems - combines both rule-based and automatic approaches.

How is machine learning used for sentiment analysis?

The primary role of machine learning in sentiment analysis is to improve and automate the low-level text analytics functions that sentiment analysis relies on. For example, data scientists can train a machine learning model to identify nouns by feeding it a large volume of text documents containing pre-defined examples. 

Once the model is ready, those training methods can be applied to building new models to identify other parts of speech. Machine learning also helps solve tricky problems caused by the evolution of language. 

Sentiment analysis in Chatbots

In the context of chatbots, sentiment analysis helps in evolving the chatbot’s emotional intelligence.

While machine learning helps to personalize the chatbot’s interactions by utilizing historical customer data, NLP helps to evaluate and interpret the data sent by the customer in real-time.

These two features collectively help chatbots to deliver relevant responses and conduct meaningful conversations. Sentiment analysis takes this a step further by enabling bots to understand human moods and emotions.

For example, for a text with a high positive score, the digital assistant can use that as an opportunity for recommending a product or sales conversion. Similarly, in the case of a high negative score, the chatbot can escalate the complaint and transfer the call to a live agent.

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