Sentiment: A Guide to Understanding Your Audience and Their Opinions


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Do you want the formula for how brands are able to connect with their audience on social media?

The start of the formula is pretty simple! By understanding how consumers feel, brands create more customer-centric content based on what their audience cares about.

One key component to this understanding is Sentiment analysis! 

Sentiment analysis is the application of natural language processing (NLP) techniques to analyze and categorize opinions expressed in text conversations based on patterns within the language. 

When applied to social media, sentiment analysis allows brands to understand the true feeling behind  how consumers are discussing brands, products, industries, or competitors. 

By analyzing every social media post revolving around a conversation, brands are able to get a sense of how positive or negative the discussion is. 

Why brands should care about sentiment on social media

Compare these two scenarios:

  • Imagine a world where a brand has no idea what people are saying about their products and industry on social media. 
  • Now imagine if that same brand was able to quickly understand the sentiment of each conversation they care about on social media. 

Which of these brands will create more customer-driven content? – Clearly, the 2nd scenario

By consistently monitoring conversations on social media, then analyzing the sentiment of each post, and finally aggregating the sentiment of every post across the conversation, brands and their social media managers are able to understand the feeling behind the conversation. 

By integrating this sentiment insights into everyday social media activity, brands will

  • Produce content tailored for their audience
  • Gain insight into their audience’s opinions
  • Understand how public perception has changed over time
  • Analyze how a social campaign has shifted sentiment
  • Identify polarizing social posts on both the positive and negative side

How to use sentiment analysis information

Incorporating sentiment analysis into social media monitoring is one of the first steps to integrating social media analytics into a social media strategy.

Social media content has no filter. The public will give you their true feelings when it comes to your latest product, campaign, or industry discussion.

However, understanding this sentiment for larger conversations can be difficult — without applying NLP techniques it can take hours to understand the sentiment around a conversation, or to even find the “right” conversation. 

By applying sentiment analysis to a conversation of interest, brands can quickly take the sentiment information and decide:

  • The conversation has overwhelming positive sentiment and jump in
  • The conversation is mainly negative, either avoid the subject in posts or feed into negativity to generate more interest
  • For example: if a competitor is receiving mostly negative sentiment, can your brand capitalize on the negativity by countering with how your product doesn’t have the same problem/issue/etc.?

Check out this article by MonkeyLearn which goes in-depth on how sentiment analysis works and the importance of taking advantage of sentiment data.

Sentiment score examples

Positive sentiment post:

“Social media analytics offer valuable data marketers can use to measure audience growth and engagement with your business.” – Sentiment score = 0.83

Neutral sentiment post:

“The analogy continues: each successive CMO that fails to invest in marketing analytics passes the buck to the next one.” – Sentiment score = -0.1

Negative sentiment post:

"Trying to create social media content without analytics is the biggest pain, how do I know my hashtags are even helping my content?” – Sentiment score = -0.7

TSV Analytics sentiment insights

TSV Analytics, also known as Topic, Sentiment, and Virality Analytics, heavily focuses on drawing insights from sentiment data. 

By monitoring social media conversations related to a brand or industry, TSV Analytics summarizes the overall sentiment for the conversation, allowing social media managers to immediately understand how their audience feels about the topic.

Using TSV Analytics to monitor a conversation allows you to see:

  • The most common positive keywords and hashtags used within the conversation
  • The most common negatively used keywords and hashtags 
  • Sentiment of micro-conversations around the overarching theme
  • Across platforms, which platform has the most positive vs. negative discussion 
  • Crisis management information, the valleys and mountains of sentiment over the past days and weeks


Sentiment analysis has become a critical tool in a social media manager’s toolbox. 

Allowing brands to understand how their audience feels at any moment allows them to create more tailored content designed for that audience based on how they feel.

Incorporating sentiment analysis to gain insight into positive/negative hashtags, sentiment around each conversation happening, and competitor analysis provides brands with an upper hand over the same competition they’re analyzing. 

How is your brand using sentiment analysis to better understand your audience?

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