Using OpenAI GPT-4 to Perform Sentiment Analysis Data Labeling

by Duncan Miller on February 16, 2024
Large Language Models (LLMs), in particular OpenAI's GPT models, have introduced a powerful new tool for businesses to understand and process natural language data. With advanced capabilities in understanding context and nuance, both GPT-3 and GPT-4 have become extremely effective for conducting sentiment analysis data labeling for various business applications. Sentiment analysis data labeling refers to the process of categorizing text data based on the sentiment expressed within it. This involves annotating texts, such as customer reviews, social media posts, or any textual content, with labels that indicate whether the sentiment is positive, negative, or neutral.

In the context of utilizing LLMs like GPT-4, this process is automated to analyze vast amounts of natural language data efficiently. This automated sentiment analysis and data labeling can empower businesses to gain faster and deeper insights into customer opinions, marketing performance, and market trends, facilitating rapid data-driven decision-making and strategy development.

The Power of GPT-4 in Sentiment Analysis

GPT-4's extensive training on diverse datasets allows it to accurately label data sentiment at scale, including customer reviews, marketing campaigns, inbound support inquiries, and even social media posts. Its ability to discern subtle differences in language tone and context makes it invaluable for businesses seeking to understand their audience better.

Benefits for Businesses

  • Enhanced Customer Insights The latest OpenAI model, GPT-4 can analyze vast amounts of data, providing businesses with nuanced customer sentiment insights, leading to improved products and services.
  • Cost-effective and Scalable LLMs offer an affordable alternative to traditional human labeling, with studies showing a potential cost reduction of 50% to 99%. By leveraging these models for data annotation, businesses can drastically lower expenses without sacrificing data quality.
  • More Accurate than Human Annotators Recent research shows that LLMs including GPT-4 can regularly outperform human annotators in terms of accuracy for data labeling and sentiment analysis.
  • Rapid and Continuous Data Annotation LLMs can annotate data at a pace unattainable by human labelers, enabling faster and more continuous analysis. This rapid turnaround is invaluable for companies aiming to stay ahead of the competition.

Enhanced Customer Insights

The GPT-4 Technical Report highlights GPT-4's ability to analyze extensive volumes of data which can revolutionize how businesses understand and engage with their customers. Through advanced natural language processing and machine learning algorithms, GPT-4 can sift through social media posts, customer reviews, forum discussions, and support tickets to extract detailed sentiment insights. This analysis enables businesses to pinpoint customer preferences, pain points, and overall sentiment towards their brand or specific products and services.

Cost-Effective and Scalable

The paper Want To Reduce Labeling Cost? GPT-3 Can Help explores leveraging GPT-3 for labeling for a variety of Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks showing a potential cost reduction of 50% to 96% to traditional human labeling. This cost-effectiveness, combined with a novel hybrid framework that mixes pseudo labels from GPT-3 with human labels, can lead to improved performance even with a limited labeling budget, offering a highly efficient and scalable methodology for data labeling applicable across multiple applications.

Additionally, a recent technical report by analyzed the cost per label for the latest LLMs against skilled human annotators and found that GPT-4 and GPT-3-5-Turbo respectively provide an 86% and 99% reduction in cost compared to skilled annotators.

Cost Per Label (source

More Accurate than Human Annotators

The paper ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks presents a comprehensive analysis showing that ChatGPT significantly outperforms crowd-workers on Amazon Mechanical Turk on various text annotation tasks, including relevance, stance, topics, and frame detection across different datasets. ChatGPT's zero-shot accuracy outperforms crowd-workers by about 25 percentage points on average across different datasets. The per-annotation cost of using ChatGPT is less than $0.003, making it approximately thirty times cheaper than using crowd-workers through MTurk.

The technical report found that for achieving the highest quality labels, GPT-4 is the best choice among out-of-the-box LLMs. GTP-4 even outperformed skilled human annotators with 88.4% agreement with ground truth, compared to 86% for skilled human annotators. GPT-3-5-Turbo only slightly underperformed skilled annotators with 81.3% agreement with ground truth.

Label quality across a variety of NLP tasks (source

Rapid and Continous Data Annotation

One of the key advantages of using LLMs for data labeling is their speed and scalability. LLMs can process and label vast amounts of data in a fraction of the time it would take human annotators or traditional automated systems. LLMs never get tired, sleep, or take a break, they can be constantly monitoring the data, enabling continuous analysis and rapid detection. The speed at which LLMs operate allows businesses to stay ahead of the competition by rapidly adapting to new data insights and trends. The technical report shows GPT-4 took an average of 2.95 seconds per label vs skilled human annotators averaging 56.3 seconds, meaning humans took 19x longer than GPT-4 on average.  The results for GPT-3-5-Turbo look even better, averaging 1.66 seconds per label, meaning humans took over 33x longer than GPT-3-5-Turbo.

Time per label (source

Implementation Examples

  • Social Media Analysis LLMs can be used to monitor various social media platforms to gauge public sentiment toward new product launches or marketing campaigns, offering real-time feedback on customer reception and engagement levels.
  • Online Reviews and Feedback By analyzing online reviews across platforms like Amazon, Yelp, or Google, GPT-4 helps businesses understand customer satisfaction levels and identify areas for improvement in products or services.
  • Support Ticket Analysis: Performing continuous deep dives into customer support tickets can quickly reveal common issues or concerns, allowing businesses to address these problems proactively and improve customer service quality.
  • Survey and Poll Analysis Analyze responses from customer surveys and polls, providing a rich source of feedback for product development, marketing strategies, and overall business direction.
  • Product Development Product companies might use sentiment analysis to understand user feedback on their products, guiding the development of new features that meet customer needs. X (Twitter) uses LLMs to analyze tweets about its platform and uses this information to improve the platform’s features.
  • Marketing Campaign Analysis Use an LLM to optimize marketing campaigns by understanding how customers interact with them, such as by analyzing customer clicks, views, and engagement.
  • Customer Recommendations and Advertising Netflix uses LLMs to analyze customer reviews of its movies and TV shows. This information is then used to recommend new content to customers. Facebook uses posts by and about its users to target its ads more effectively.
  • Brand Reputation Monitoring Companies can social media sentiment to manage their brand reputation effectively, responding proactively to negative sentiments.
  • Market Trend Analysis By analyzing broader market trends on social media and online forums, GPT-4 helps businesses stay ahead of market trends.
  • Competitive Analysis Sentiment analysis can also help businesses gauge public sentiment toward competitors, offering insights into strengths, weaknesses, opportunities, and threats.

Challenges and Considerations

Employing Large Language Models (LLMs) like GPT-4 for sentiment analysis introduces unparalleled advantages but also necessitates navigating certain intricacies to fully leverage their potential. Key among these challenges is the model's handling of ambiguous data, which could lead to misinterpretation of nuanced sentiments. Additionally, the dynamism of language and societal discourse demands that the model remains current to ensure its analyses remain relevant and reflective of contemporary usage.

  • Inherent Data Biases Large Language Models (LLMs) are susceptible to adopting the biases present in their training datasets. This predisposition can result in the generation of labels that are inadvertently biased, affecting the objectivity and fairness of the analysis.
  • Need for Ongoing Oversight To maintain their efficacy and reliability, LLM integrations require continuous monitoring and maintenance to ensure that they provide accurate sentiment analysis, as the performance may degrade over time with model drift.
  • Overconfidence One notable challenge with LLMs is their tendency to display overconfidence in their outputs. This characteristic means that LLMs might assign labels with unwarranted certainty, even in instances where the predictions are incorrect, potentially misleading downstream decision-making processes.

Hybrid Approach

A strategic approach to surmount these obstacles involves adopting a hybrid model that marries the computational efficiency of LLMs with the discerning judgment of human annotators. The hybrid approach, integrating human oversight, plays a critical role in refining the model's output. It provides a mechanism for correcting inaccuracies, adding a layer of nuanced understanding, and identifying biases that the model alone might overlook. This collaborative interaction between LLMs and human expertise not only enhances the accuracy of sentiment analysis but also fortifies the model's fairness and adaptability to evolving linguistic trends.

Capturing Valuable Data for Fine Tuning

While more labor intensive, a hybrid approach with human annotation can be used to develop an ongoing process of model fine-tuning, enabling companies to develop their own proprietary, diverse, and contemporary dataset that mirrors the latest linguistic and societal developments. This ensures the model's training base is broad enough to minimize biases and adaptable to the fluidity of language use.

Capturing the Strategic Advantage

GPT-4's capabilities in sentiment analysis and semantic data labeling offer businesses unprecedented insights into customer sentiments and market dynamics. Businesses that adopt this innovative approach will not only optimize their operations but also gain a competitive edge in leveraging AI for strategic advantages.

How Shiro Can Help

The Shiro platform provides a dev environment for prompt engineering and can assist businesses in several ways as they implement sentiment analysis data labeling strategies.
  • Rapid Prompt Engineering Prototyping The workshops feature in Shiro provides prompt engineers with an environment to quickly test and iterate on prompt variations, running prompts against a suite of test cases and quantitative evaluation metrics to iteratively measure and improve prompt effectiveness.
  • Prompt Performance Monitoring Promts deployed to production environments can be continuously monitored against sets of quantitative evaluation metrics. This helps teams keep an eye on LLM completion quality over time and monitor for model drift. Prompts that fall out of performance thresholds can then be revised, tested, and redeployed without changing the production code.
  • Data Collection and Annotation for Fine Tuning Completion responses generated through the Shiro API are stored, enabling teams to capture explicit feedback of completion responses either programmatically (user thumbs up / thumbs down feedback) or manually (human review). Edge cases can be converted into new tests to run against future prompt iterations and new prompts can be backtested against all previous queries. Shiro also enables implementing hybrid annotation and passing all LLM completion responses through human annotators. If the LLM completion response is incorrect, a human-corrected value can be stored on each exchange for future model fine-tuning.
In conclusion, LLMs like OpenAI GTP-3 and GPT-4 are transforming the landscape of sentiment analysis by offering a cost-effective, efficient, and accurate method for data labeling and sentiment analysis with a wide variety of business applications. By leveraging LLMs and Shiro's Prompt Engineering Platform companies can use semantic data labeling to make data-driven decisions, enhance customer experiences, and maintain a competitive edge in their industry. As LLMs continue to evolve, their role in business analytics and decision-making processes will only grow more significant, marking a new era in AI-driven business intelligence.
  • Photo of Duncan Miller

    Duncan Miller

    Founder, Software Developer

    Duncan is the founder and lead software developer for OpenShiro. He been running startups since 2006 and has been writing code for over 20 years. Duncan has an MBA from Babson College and lives with his wife and two children in Portland Oregon on an extinct cinder code volcano. He is passionate about artificial intelligence, climate solutions, public benefit companies and social entrepreneurship.

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