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.
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.
Compare these two scenarios:
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
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:
Check out this article by MonkeyLearn which goes in-depth on how sentiment analysis works and the importance of taking advantage of sentiment data.
“Social media analytics offer valuable data marketers can use to measure audience growth and engagement with your business.” – Sentiment score = 0.83
“The analogy continues: each successive CMO that fails to invest in marketing analytics passes the buck to the next one.” – Sentiment score = -0.1
"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, 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:
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?