Price as low as $4499 | Learn to build custom large language model applications

2023 sentiment analysis: Marketing with large language models (LLMs)

September 12, 2023

Sentiment analysis, a dynamic process, extracts opinions, emotions, and attitudes from text. Its versatility spans numerous realms, but one shining application is marketing.

Here, sentiment analysis becomes the compass guiding marketing campaigns. By deciphering customer responses, it measures campaign effectiveness.

The insights gleaned from this process become invaluable ammunition for campaign enhancement, enabling precise targeting and ultimately yielding superior results.

In this digital age, where every word matters, sentiment analysis stands as a cornerstone in understanding and harnessing the power of language for strategic marketing success. It’s the art of turning words into results, and it’s transforming the marketing landscape.

Supercharging Marketing with Sentiment Analysis and LLMs
Supercharging Marketing with Sentiment Analysis and LLMs

Under the lens: How does sentiment analysis work?

Sentiment analysis typically works by first identifying the sentiment of individual words or phrases. This can be done using a variety of methods, such as lexicon-based analysis, machine learning, or natural language processing.

Once the sentiment of individual words or phrases has been identified, they can be combined to determine the overall feeling of a piece of text. This can be done using a variety of techniques, such as sentiment scoring or sentiment classification.

Large language model bootcamp

 Sentiment analysis and marketing campaigns

In the ever-evolving landscape of marketing, understanding how your audience perceives your campaigns is essential for success. Sentiment analysis, a powerful tool in the realm of data analytics, enables you to gauge public sentiment surrounding your brand and marketing efforts.

Here’s a step-by-step guide on how to effectively use sentiment analysis to track the effectiveness of your marketing campaigns:

1. Identify your data sources

Begin by identifying the sources from which you’ll gather data for sentiment analysis. These sources may include:

  • Social Media: Monitor platforms like Twitter, Facebook, Instagram, and LinkedIn for mentions, comments, and shares related to your campaigns.
  • Online Reviews: Scrutinize reviews on websites such as Yelp, Amazon, or specialized industry review sites.
  • Customer Surveys: Conduct surveys to directly gather feedback from your audience.
  • Customer Support Tickets: Review tickets submitted by customers to gauge their sentiments about your products or services.

2. Choose a sentiment analysis tool or service

Selecting the right sentiment analysis tool is crucial. There are various options available, each with its own set of features. Consider factors like accuracy, scalability, and integration capabilities. Some popular tools and services include:

  • IBM Watson Natural Language Understanding
  • Google Cloud Natural Language API
  • Microsoft Azure Text Analytics
  • Open-source libraries like NLTK and spaCy
Sentiment analysis and marketing campaigns
Sentiment analysis and marketing campaigns – Data Science Dojo


Read more –> LLM Use-Cases: Top 10 industries that can benefit from using large language models


3. Clean and prepare your data

Before feeding data into your chosen tool, ensure it’s clean and well-prepared. This involves:

  • Removing irrelevant or duplicate data to avoid skewing results.
  • Correcting errors such as misspelled words or incomplete sentences.
  • Standardizing text formats for consistency.


4. Train the sentiment analysis tool

To improve accuracy, train your chosen sentiment analysis tool on your specific data. This involves providing labeled examples of text as either positive, negative, or neutral sentiment. The tool will learn from these examples and become better at identifying sentiment in your context.


5. Analyze the Results

Once your tool is trained, it’s time to analyze the sentiment of the data you’ve collected. The results can provide valuable insights, including:

  • Overall Sentiment Trends: Determine whether the sentiment is predominantly positive, negative, or neutral.
  • Campaign-Specific Insights: Break down sentiment by individual marketing campaigns to see which ones resonate most with your audience.
  • Identify Key Topics: Discover what aspects of your products, services, or campaigns are driving sentiment.


6. Act on insights

The true value of sentiment analysis lies in its ability to guide your marketing strategies. Use the insights gained to:

  • Adjust campaign messaging to align with positive sentiment trends.
  • Address issues highlighted by negative sentiment.
  • Identify opportunities for improvement based on neutral sentiment feedback.
  • Continuously refine your marketing campaigns to better meet customer expectations.


Large Language Models and Marketing Campaigns



Use case


Create personalized content Use an LLM to generate personalized content for each individual customer, such as email newsletters, social media posts, or product recommendations.
Generate ad copy Use an LLM to generate ad copy that is more likely to resonate with customers by understanding their intent and what they are looking for.
Improve customer service Use an LLM to provide more personalized and informative responses to customer inquiries, such as by understanding their question and providing them with the most relevant information.
Optimize marketing campaigns Use an LLM to optimize marketing campaigns by understanding how customers are interacting with them, such as by tracking customer clicks, views, and engagement.

Benefits of using sentiment analysis to track campaigns

There are many benefits to using sentiment analysis to track marketing campaigns. Here are a few of the most important benefits:

  • Improved decision-making: Sentiment analysis can help marketers make better decisions about their marketing campaigns. By understanding how customers are responding to their campaigns, marketers can make more informed decisions about how to allocate their resources.
  • Increased ROI: Sentiment analysis can help marketers increase the ROI of their marketing campaigns. By targeting campaigns more effectively and optimizing ad campaigns, marketers can get better results from their marketing spend.
  • Improved customer experience: Sentiment analysis can help marketers improve the customer experience. By identifying areas where customer satisfaction can be improved, marketers can make changes to their products, services, and marketing campaigns to create a better experience for their customers.

Real-life scenarios: LLM & marketing campaigns

LLMs have several advantages over traditional sentiment analysis methods. They are more accurate, can handle more complex language, and can be trained on a wider variety of data. This makes them well-suited for use in marketing, where the goal is to understand the nuances of customer sentiment.

One example of how LLMs are being used in marketing is by Twitter. Twitter uses LLMs to analyze tweets about its platform and its users. This information is then used to improve the platform’s features and to target ads more effectively.

Another example is Netflix. Netflix uses LLMs to analyze customer reviews of its movies and TV shows. This information is then used to recommend new content to customers and to improve the overall user experience.



Sentiment analysis is a powerful tool that can be used to track the effectiveness of marketing campaigns. By understanding how customers are responding to their campaigns, marketers can make better decisions, increase ROI, and improve the customer experience.

If you are looking to improve the effectiveness of your marketing campaigns, I encourage you to consider using sentiment analysis. It is a powerful tool that can help you get better results from your marketing efforts.

Sentiment analysis is the process of identifying and extracting subjective information from text, such as opinions, appraisals, emotions, or attitudes. It is a powerful tool that can be used in a variety of applications, including marketing.

In marketing, sentiment analysis can be used to:

  • Understand customer sentiment towards a product, service, or brand.
  • Identify opportunities to improve customer satisfaction.
  • Monitor social media for mentions of a brand or product.
  • Target marketing campaigns more effectively.

In a nutshell

In conclusion, sentiment analysis, coupled with the power of Large Language Models, is a dynamic duo that can elevate your marketing strategies to new heights. By understanding and acting upon customer sentiments, you can refine your campaigns, boost ROI, and enhance the overall customer experience.

Embrace this technological synergy to stay ahead in the ever-evolving world of marketing.


Register today

Up for a Weekly Dose of Data Science?

Subscribe to our weekly newsletter & stay up-to-date with current data science news, blogs, and resources.

Data Science Dojo | data science for everyone

Discover more from Data Science Dojo

Subscribe to get the latest updates on AI, Data Science, LLMs, and Machine Learning.