The Gist
- Evolution of sentiment analysis. Sentiment analysis has grown from basic computational linguistics to AI-powered customer insights.
- Social media’s role. Social platforms introduced real-time sentiment tracking, but API changes have complicated access.
- AI’s transformative impact. AI tools enable faster, more accurate sentiment analysis for marketers to act decisively on customer insights.
Marketing technology is a rapidly changing world, and marketers are finding themselves considering a variety of methodologies based on that technology all the time. One methodology that has emerged to provide consistent value is sentiment analysis. Sentiment analysis is used for understanding customer perceptions and experiences. It has been considered for large documentation until social media enhanced where it can be applied.
Now the evolution in sentiment analysis has arrived: the transformative potential of artificial intelligence in uncovering nuanced customer insights. This post will explain how sentiment analysis
Table of Contents
Where Customer Sentiment Got Its Start
Sentiment analysis is a computation process for identifying an overall tone expressed in a given body of text.
Understanding the Basics of Sentiment Analysis
It is a text analysis that relies on a combination of natural language processing and computational linguistics that changes large bodies of sentences and paragraphs into tokens. Tokens are individual words or portions of words. Breaking down the text into words allows them to be categorized against subjective expressions. The process answers what sentiment is expressed within a large document or extensive volume of comments.
The foundation of sentiment analysis has existed since the 1960s.
The Early Days of Computational Linguistics
The analysis emerged among the first attempts at computational linguistics. Early researchers were primarily interested in how computers could interpret subjective language and understand emotional content within a body of text.
Computer processor capacity increased in the 1990s.
How NLP and Computational Power Transformed Sentiment Analysis
The increased computational power granted academic researchers access to more systematic analysis frameworks. More inferential statistical analysis could be applied to tokens to draw more understandable conclusions, while natural language processing (NLP) allowed more sophisticated methods to describe text classifications.
Related Article: Emotion Is the New Metric: The Rise of Sentiment Analysis in Retail
Social Media Brought Real-Time Sentiment Analysis to Marketing
A significant breakthrough came with the rise of web content and social media.
Real-Time Sentiment Analysis Meets Social Media
The internet introduced real-time sources of text for researchers and companies to investigate — social media posts and their subsequent responses were treated as data to be tokenized. Researchers and analysts shifted focus towards text generated from social media and blog posts.
Using programming languages like R and Python, analysts learned to import social media text into data, and then enable a program to apply the advanced statistics needed to categorize each word.
The Role of APIs in Social Media Sentiment Analysis
APIs — portals that connect applications — further made data access convenient.
Analysts could import text data more readily into sentiment analysis programming models.
The digital aspects of sentiment analysis let marketers rapidly analyze the sentiment around the frequency mention of a brand, product or service. The result was advanced sentiment analysis techniques applied to customer reviews, social media feedback and public perception of a brand.
For example, imagine a brand having created a large Twitter community around a hashtag that a marketing team has used for a social media campaign. A marketing analyst would import tweet data through the Twitter API and then examine the sentiment around that hashtag. The analysis could also reveal other phrases that are frequently mentioned, guiding ideas on how to respond to that campaign sentiment.
Analysis like this enables marketing managers to quickly determine how customers felt about a product, service and decide faster how to best manage customer interest or protect a brand image.
Related Article: Memorable Marketing Campaigns This Year
Challenges in Social Media Sentiment Analysis After Twitter’s API Changes
The growth of sentiment analysis hit a snag, however, when the most popular platform to analyze comments, Twitter, changed its API access.
Twitter allowed free access to the payload from its API endpoints. The API payloads contained data which included posts, hashtags and other metadata. The data formed the basis of a Twitter ecosystem of additional services and solutions. The easy access allowed analysts and social scientists to consider posts as an accurate real-time representation of the feeling people had towards topics, including commercial subjects.
When Elon Musk acquired Twitter and renamed it X in 2022, the platform changed its API policy such that the APIs had a low rate limit. This limited the amount of data that was available for third party API users. Higher rates were restricted to pricing tiers, where users pay for more data access. Since then X announced a price increase in October.
The pricing tiers forced many third-party platforms to shut down. Their applications, meant to complement Twitter usage, were dependent on the API data points to provide service to customers. Most important, the pricing tier structure also eliminated many research interests from organizations that were studying sentiment analysis based on social media posts.
The Next Level Sentiment Through AI
An evolutionary opportunity to apply sentiment analysis has emerged with AI.
AI Assistants as a Game Changer for Sentiment Analysis
Customer interactions with AI assistants are now a viable source for text that illustrates customer sentiment. Customers had treated specific social media platforms as channels to ask questions. I remember seeing a Macy’s customer asking a customer service Twitter profile about her jacket’s availability.
Customers are now bringing the same treatment to AI assistants for asking questions and receiving guidance on their needs. AI assistants create a dialog environment that is less noisy and more intentional than a dialog on social media platforms. The text can be analyzed with a clear line of conversation identified as opposed to the open nature of social media. This makes customer conversations through AI assistants a more precise expression of sentiment.
AI assistant conversations give marketers a competitive edge for processing sentiment faster. Because customers are growing accustomed to using AI assistants, marketers are gaining more isolated data to understand if customers are feeling good about their exchanges or poorly about an unresolved incident.
Embedding Text in Large Language Models for Faster Insights
The data can be placed within embeddings in Large Language Models (LLM).
Embeddings are high-dimensional vectors that provide a specialized place to store content. The high dimensionality of embedding features creates a dense representation, capturing the semantic meaning of words, sentences or even entire documents from an AI query.
AI relies on embeddings to represent each word of a text and associated media in a numerical form that the model can understand and process. The speed of learning text conversion and sentiment can be completed faster. That changes into a competitive advantage. The faster you can gather insights, the more value your actions can deliver.
How Should Marketers Approach AI Within Sentiment Analysis?
The inclusion of AI in sentiment analysis offers innovations to text analysis, yet marketers face the same planning steps for a sentiment analysis as they would for any other program. Marketers need to understand the basics of sentiment analysis to effectively guide the AI model.
Establishing Parameters for AI in Sentiment Analysis
Marketers should transform their sentiment guidelines into parameters that an AI model should maintain when processing the words.
The setting of parameters can be done in various frameworks designed for establishing word embeddings, such as Word2Vec, GloVe and FastText.
Tools and Frameworks for Setting Up Sentiment Analysis
Transformers, the neural network architecture behind LLMs like ChatGPT and Gemini, can also be adjusted, setting parameters to address how words are tokenized. Creation techniques vary; Each transformer has algorithms for analyzing the tokened words, while each framework has specific statistics for identifying how the tokened words should be categorized. All of these options are available on top of libraries in R programming and Python which can visualize the data or place it within a dashboard for review.
Marketers should also look out for prompt methods of using generative AI for sentiment analysis.
Leveraging Generative AI for Streamlined Sentiment Analysis
Claude, ChatGPT, Propensity, Gemini and other generative AI models are improving their ability to work with statistical calculations and consider multimodal data within their prompt replies. This means marketers can use natural language to describe the desired analysis parameters, saving time in setting up programming to conduct sentiment analysis.
Marketers should be excited about the expanded choices for conducting sentiment analysis with AI. Sentiment analysis is a predictive analysis that can seem complex due to the variety of choices available.
But AI offers an opportunity to streamline those choices so that marketers can better apply customer sentiment analysis about a brand, product, or service. Marketers now have a great way to transform customer sentiment into their best brand response.
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