Why Sentiment Analysis On Comments Matters For Your Business

Social media comments sentiment analysis helps you discover hidden gems of business intelligence. It helps you set competitor benchmarks, track your performance based on customer satisfaction, get product insights, and manage your brand reputation. The trick is to know how to leverage these customer comments and social media chatter and convert them into relevant, actionable insights. In this article, we tell you how.

Do Social Media Comments Matter?

Social media is a very important tool to grow your business. It’s one of the most economical ways of doing market research through customer feedback analysis and a time-efficient one at that. Further, it allows you to engage with customers - both current and potential - in a worthwhile manner. It helps you build your brand image and address any negative or sensitive issues in a timely manner.

When you analyze sentiment in social media comments, you get granular insights about different facets of your business that affect your profits. These include the pros and cons of your product or service, changing market preferences, your after-sales service, store locations and hours, and much more.

In fact, a study by Hootsuite found that 70% of companies use social media insights to formulate their brand strategy, and more than 60% find comments sentiment analysis data extremely useful for successful lead conversions and sales.

How Does Comments Sentiment Analysis Work?

Comments sentiment analysis for customer experience analysis is done through machine learning (ML) models that use several ML tasks in tandem. These include neural networks, native natural language processing (NLP), named entity recognition (NER), semantic clustering, and finally, sentiment analysis.

To ensure that you get the most accurate results, you need to use an aspect model that is best suited to your industry. Most off-the-shelf models are not made specifically for your business, and this can result in the loss of important insights as the model will not be able to recognize those aspects and entities. Therefore, the comments analysis tool you choose must be trained with training data based on your business.

Below are the 4 main steps in which sentiment analysis on comments takes place.

Step 1 - Data collection

The comments data from social channels can be collected in two ways:

a. The data can either be uploaded on the comments analysis platform’s dashboard directly by pasting the URL of the social media link. Or, if you have the API key of the social media platform you can also scrape the comments. This is especially useful for real-time sentiment analysis.

b. The other way is to download the comments onto an excel file and then upload them manually onto the platform.

Step 2 - Data Processing

In this stage, the comments are processed for text analysis. All the non-text data is removed, except hashtags and emojis. It is important that the sentiment analysis tool you choose does not ignore emojis as they can play a very important part in getting the right insights. Correct analysis of emojis ensures that you don’t get false negatives and positives in your brand experience and customer insights.

Step 3 - Data Analysis

Now that the text data is cleaned and prepped for comments sentiment analysis, it goes through various ML techniques that are necessary for accurate interpretation. These include:

• Custom tagging Aspects in the data are manually tagged so that sentiment is captured and assigned to the right aspect (price, service, product) mentioned in the data.
• Multilingual analysis Part-of-speech taggers for every language work individually to derive meaning and intent from comments. This removes the need for translations, which further assists in increasing the accuracy of the insights.
• Topic classification Comments are segregated into different categories and topics such as customer service, price, value for money, etc.
• Sentiment Analysis The platform processes the data for sentiment, which is scored within a range of -1 to +1. You can get overall customer and product sentiment scores as well as aspect-based scores from the social media platform of your choice.

Step 4 - Insights visualization

All insights are now produced on the visualization dashboard either in percentile form or in the form of charts and graphs. The dashboard allows you to set alerts based on keywords or hashtags as well so you get instant notifications for any brand mentions or spike in posts for certain hashtags for business.

The dashboard enables you to see time-based sentiment trends, sentiment based on languages and data sources, and much more. Thus you can get almost real-time tracking of social media comments for your brand campaigns.

Whether it is TikTok insights or comments sentiment analysis on Twitter feeds, you get all your information within seconds.

Learn more about sentiment analysis process.

How Are Businesses Using Sentiment Analysis On Comments?

Below are some of the most important ways in which businesses are using sentiment analysis on comments. These include extracting in-depth customer and product insights, discovering emerging market trends, cross-selling, increasing market share, and more.

1. Monitor overall customer satisfaction
2. Improve customer experience
3. Gain real-time consumer insights
4. Identify emotional triggers in customers
5. Better products and services
6. Leverage upselling opportunities
7. Build brand loyalty
8. Reduce customer churn
9. Train customer service staff
10. Train chatbots
11. Manage brand reputation
12. Monitor sentiment over time
13. Improve social media marketing content
14. Get competitor knowledge
15. Prepare for emerging market trends

Read more about how businesses are using social media comments sentiment analysis.

Conclusion

Repustate’s AI-powered solution for social comments sentiment analysis, Repustate IQ, has helped numerous companies the world over, gain important insights from social media to revive their growth strategies and boost sales conversions. From brands like Abercrombie & Fitch and Tinder, to our clients in the banking and healthcare sector, all use Repustate IQ based on their specific industries, languages, and flexible installation choice (on-prem or Cloud-based).

Repustate IQ is granular, accurate, and targeted. It quickly integrates with your existing tech stack and starts analyzing data for insights within a few clicks. There’s no coding required so you can adjust aspects and topics as and when your business needs demand it. More importantly, it is infinitely scalable and capable of analyzing 1,000 comments per second.