Why sentiment analysis is important for social media? -

Why sentiment analysis is important for social media?

Social networks like Twitter, Facebook and Instagram are where customers often leave instant feedback, making them a valuable source of immediate reaction for businesses.

However, with the rise of social messaging, it’s no longer possible to manually monitor conversations on social media. Here’s where sentiment analysis helps speed up the process by automatically putting customer feedback into context.

What is sentiment analysis?

Sentiment analysis (also known as opinion mining) is a machine learning task to find out what customers think about your business.

Social media sentiment analysis focuses on customer conversations in the social sphere and helps businesses monitor the sentiment of their brand and understand negative or positive sentiments towards their products and services. In short, sentiment analysis is a social listening tool that tells you in real time what your customers like and dislike about your company.

Thanks to natural language processing (NLP) and machine learning algorithms, sentiment analysis tools can understand complex linguistic structures and classify text in the same way humans do.

Combining social media sentiment analysis tools with incoming social media data enables scalable, reliable, and accurate socialization detection and analysis.

Why sentiment analysis in social media is important

Positive social messages about a brand increase the likelihood that customers will buy or remain loyal. So it’s important to know what you and your competitors are doing well. Negative comments, on the other hand, help companies identify customers’ weaknesses and improve their products and services.

Social interactions show no signs of slowing down, and you need to use sentiment analysis tools to capture all the positive and negative social messages to better target your business.

Here are some reasons why social media sentiment analysis is important.

Constantly available customer service

Brands can monitor mentions in real time so they can respond to positive and negative mentions immediately.

Not only does this show that you are paying attention to your customers’ needs, but it also improves the customer experience by allowing you to respond in a timely manner.

By responding to “urgent” comments or identifying spikes in negative comments on social media, you can turn a bad situation around and improve both your business and your customers. Imagine being able to quickly identify a spike in negative mentions on social media and use all of your resources to turn the situation around.

Scalability of social data analysis

Sentiment analysis tools can process massive amounts of social data in seconds and can easily scale up (or down). You can meet customer demands without worrying about the cost of hiring new staff, even if large amounts of data is generated due to seasonal fluctuations or new product launches.

Real-time product feedback

Customers can tell you directly on social media what they like and don’t like. You can get instant feedback on marketing campaigns and product launches, in order to identify roadblocks right away.

You may notice an increase in negative social posts about poor performance. Share this information with the product team so they can resolve these issues as quickly as possible. On the other hand, customers may praise new features.

Either way, feedback analysis can help you figure out which features customers like, which areas of the business need improvement, or alert you to bugs or flaws that need to be fixed.

Consistent analysis and accurate knowledge

Manually categorizing social data by sentiment can lead to inconsistencies. Humans are prone to error, especially in repetitive tasks. Not only that, it is subjective. In contrast, automated sentiment analysis models based on machine learning are trained with a single set of rules and apply the same set of criteria to every text.

Training a social media sentiment analysis model to correctly understand the difference between negative and positive mentions can ensure that the model provides accurate information.

Tracking competitors for market research

It’s always a good move to keep an eye on what your competitors are doing (good or bad). By analyzing sentiment, you can compare how customers mention topics on social media platforms and find out if there are any areas, features, or products that need improvement.

Analyzing competitors’ conversations on social media can lead to new business opportunities. Could you offer a better product or service that meets your customers’ needs, or is there a feature your customers like, that you could incorporate into your own product?

Real-time sentiment analysis in social media

Tools like MonkeyLearn make it easy to get started with sentiment analysis. MonkeyLearn specializes in text analytics and offers a variety of pre-trained machine learning models for sentiment analysis, intent detection, topic labeling, and feature extraction.

You can also train your own models using an intuitive user interface and industry-specific training data. Learn how to use our sentiment analysis tool to analyze social media mentions in three easy steps.

Collecting social data
Preparing data from social media
Track your feed

Conclusion

Social media interactions are a valuable source of information. They can help you determine how your customers feel about your brand and how you can improve your business.

Using sentiment analysis tools to understand unstructured data like tweets, Facebook comments, and Instagram posts can provide actionable insights that help you make smart decisions.

MonkeyLearn makes it easy to perform sentiment analysis on social media data, whether you’re using already trained models or creating your own.