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Brand Sentiment Analysis

Reading Time: 6 minutes You may have heard about brand sentiment analysis, but like me didn’t know much depth…

Written by Joe Robison
Last updated 7 months ago

Reading Time: 6 minutes

You may have heard about brand sentiment analysis, but like me didn’t know much depth about it versus the surface level concept.

Before research, if I were to define it, I would say it’s a periodic or constant collection, processing and analysis of what your ideal target market thinks of your brand. As I understand it’s measured both qualitatively and quantitatively. I would imagine if it’s lower than a certain threshold, brand damage occurs. I would also imagine there are diminishing returns after a certain level, but perhaps there’s an infinite limit, which would lead to non-monopolistic mega brands like Google and Amazon and Apple.

After deeper research, this is what I found.

What is brand sentiment analysis?

Brand sentiment analysis, also known as opinion mining, is the use of natural language processing (NLP), text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In the context of a brand, it refers to understanding how the public or its target audience perceives it based on data collected from various sources.

The data for brand sentiment analysis could come from numerous sources such as social media posts, reviews, blog posts, forum discussions, and more. Essentially, it includes any place where consumers may share their opinions or feelings about a brand.

Brand sentiment analysis typically classifies sentiments into positive, negative, and neutral. Some models may include more nuanced classifications such as “very positive,” “somewhat positive,” “neutral,” “somewhat negative,” and “very negative.”

This analysis provides valuable insights to businesses, helping them understand how their brand, products, or services are perceived. It can help in identifying issues early, understanding the impact of marketing and PR campaigns, improving products and services based on feedback, and more.

As it involves understanding context, sarcasm, and nuances in language, brand sentiment analysis is a complex task often performed with machine learning and AI techniques.

How do best in class Fortune 500 companies do brand sentiment analysis?

Brand sentiment analysis is a complex task that involves a range of techniques, tools, and approaches. Here’s an overview of how best-in-class Fortune 500 companies might approach it:

Overview:
These companies typically use a combination of automated and manual processes to analyze brand sentiment. They use sophisticated machine learning and AI tools to analyze large volumes of data from various sources like social media, customer reviews, news articles, and more. They complement this with human analysis to understand nuances and context better.

Theory:
The underlying theory of brand sentiment analysis is rooted in natural language processing (NLP), computational linguistics, and machine learning. The aim is to understand the polarity (positive, negative, neutral) of the language used in reference to the brand, as well as more nuanced emotions.

Operating Frameworks:
These companies usually have a dedicated team for sentiment analysis and related tasks. They operate in a cross-functional manner, collaborating with marketing, customer service, PR, and product teams. They also often work with external vendors and partners for specific tasks.

Methods:
Methods for brand sentiment analysis typically involve data collection, pre-processing, sentiment scoring, and analysis.

  1. Data Collection: Data is collected from various sources like social media, customer reviews, surveys, and news articles. APIs, web scraping tools, and data partnerships are often used for this.
  2. Pre-Processing: The collected data is cleaned and pre-processed. This may involve removing irrelevant information, correcting spelling errors, and converting the text into a form suitable for analysis.
  3. Sentiment Scoring: The pre-processed text is then analyzed to assign sentiment scores. This often involves machine learning algorithms that have been trained on labeled data.
  4. Analysis: The sentiment scores are analyzed to draw insights. This could involve looking at trends over time, comparing sentiment across different products, or identifying the impact of specific events on sentiment.

Tools:
Tools used for brand sentiment analysis vary widely. They may include social media listening tools like Brandwatch, Sprinklr, or Hootsuite, specialized sentiment analysis tools like IBM Watson Tone Analyzer, and more general data analysis tools like Python libraries (e.g., NLTK, TextBlob) or R for custom analysis.

Strategy:
The strategy for brand sentiment analysis involves integrating it into broader business and marketing strategies. The insights drawn from sentiment analysis can inform product development, customer service, marketing campaigns, crisis management, and more.

Measurement:
Measurement involves tracking key metrics over time. This could include the overall sentiment score, the volume of mentions, the share of voice compared to competitors, and more. It’s also important to track how these metrics correlate with business outcomes.

Analysis:
The analysis stage involves digging deeper into the data to understand the “why” behind the numbers. This could involve qualitative analysis of negative reviews to identify common issues or analyzing the sentiment of social media posts before and after a marketing campaign.

Improvement Processes:
Based on the insights drawn from the analysis, companies can identify areas of improvement. This could involve improving a product based on customer feedback, training customer service staff to better handle common complaints, or adjusting marketing messages to better resonate with customers.

Overall, it’s important to note that brand sentiment analysis is an ongoing process, not a one-time task. The best-in-class companies constantly monitor sentiment, adapt their strategies based on insights drawn, and continuously refine their methodologies and tools.

What about for mid-market firms?

Mid-market businesses may not have the same resources as Fortune 500 companies, but they can still effectively conduct brand sentiment analysis. The process might look a bit different, but the underlying principles are similar.

Overview:
Mid-market businesses usually focus on cost-effective and efficient methods to gather and analyze data. They might use a combination of in-house efforts and outsource certain tasks to specialized agencies or consultants.

Theory:
Just like larger organizations, mid-market businesses use the principles of natural language processing, computational linguistics, and machine learning to analyze brand sentiment. However, they might rely more heavily on pre-built tools and platforms rather than developing custom solutions.

Operating Frameworks:
In mid-market businesses, the responsibility of brand sentiment analysis often falls on the marketing or customer service team. They might also bring in a data analyst or hire an external agency for specialized tasks.

Methods:
The methods for brand sentiment analysis remain the same – data collection, pre-processing, sentiment scoring, and analysis. However, the scale might be smaller, and the tools used might be more cost-effective and user-friendly.

  1. Data Collection: Mid-market businesses collect data from similar sources – social media, reviews, surveys, etc. However, they might rely more on tools that aggregate this data for them.
  2. Pre-Processing: This stage might be simplified, using the pre-processing capabilities of sentiment analysis tools rather than conducting extensive pre-processing in-house.
  3. Sentiment Scoring: Mid-market businesses are likely to use pre-built sentiment analysis tools for this stage. They might use user-friendly platforms that don’t require extensive data science expertise.
  4. Analysis: The analysis will likely be more focused and specific due to resource constraints. Instead of analyzing all available data, a mid-market business might focus on the most relevant data sources or the most critical time periods.

Tools:
Mid-market businesses often use more cost-effective and user-friendly tools. This might include social media management tools like Buffer or Hootsuite, user-friendly sentiment analysis tools like MonkeyLearn, or even built-in analytics on social media platforms.

Strategy:
The strategy for a mid-market business would involve integrating sentiment analysis into their broader business strategy, just like a larger organization. However, they might need to be more selective about which insights to act on due to resource constraints.

Measurement:
Measurement for mid-market businesses might focus on a smaller set of key metrics, such as overall sentiment score or the number of positive and negative mentions. They might also pay more attention to direct feedback from customers, such as reviews or survey responses.

Analysis:
Analysis would involve understanding the reasons behind the sentiment scores and identifying trends. However, due to resource limitations, mid-market businesses might need to prioritize which aspects to analyze in depth.

Improvement Processes:
Based on the insights drawn from the analysis, mid-market businesses can identify areas of improvement. This could involve product improvements, customer service training, or adjustments to marketing messages. The key here is prioritizing the most impactful changes due to limited resources.

While the scale and resources might be different, mid-market businesses can still effectively use brand sentiment analysis to gain insights and drive improvements. The key is to be strategic and focused, making the most of available resources.

How might this apply for select small business industry segments?

Absolutely, here are the summaries for each industry:

  1. B2B SaaS: Small B2B SaaS companies can utilize sentiment analysis to monitor feedback from clients on platforms like LinkedIn or in user reviews, helping them understand how their software is perceived and where improvements could be made. Insights could inform product development, customer service responses, and targeted marketing strategies.
  2. B2B Services: For small B2B service providers, sentiment analysis could be used to understand clients’ feedback and experiences through channels like email surveys or social media. This information could help refine service offerings, enhance client communication, and improve overall client satisfaction.
  3. Consumer Internet: In the consumer internet sector, sentiment analysis can provide valuable insights about users’ experiences with an online platform or service, gathered from user reviews, social media posts, and other online feedback channels. This can guide user experience improvements, marketing messages, and customer support practices.
  4. Streaming Media: For small streaming media businesses, sentiment analysis can help understand audience preferences and reactions to content based on comments and reviews on the platform itself or on social media. These insights can guide content acquisition or production decisions and promotional strategies.
  5. Media: Small media companies can use sentiment analysis to gauge public reaction to their content or journalism, using data from comments sections, social media, and emails. This can inform editorial decisions, audience engagement strategies, and even crisis management in response to public sentiment.
  6. Online Security: Small businesses in the online security sector can apply sentiment analysis to understand customers’ trust levels and concerns based on social media discussions, blog comments, and reviews. This can guide product improvements, communication strategies, and educational content around security issues.
  7. Informational Services: For informational service providers, sentiment analysis can reveal how users perceive the value, accuracy, and usefulness of the information provided, using feedback from user reviews, comments, and social media discussions. These insights can lead to enhancements in information quality, delivery methods, and user interface design.
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