Pre DevelOPMENT Study 1

In a digital era overflowing with text – from social media posts and customer reviews to support chats – understanding linguistic patterns has become crucial for businesses. Marketers and media analysts can no longer manually sift through millions of messages for insights. (On X/Twitter alone, users send roughly 500 million tweets each day 1 .) This is where linguistic pattern analysiscomes in: by examining recurring language structures and trends, organizations can uncover hidden meanings, public sentiment, and emerging topics that influence their brand or strategy. Recent breakthroughs in AI, particularly Large Language Models (LLMs), have dramatically enhanced our ability to analyze such patterns on a scale.

Sequence Media & Marketing recognizes that harnessing LLM-driven pattern analysis could unlock richer insights for clients and partners. This preliminary concept study provides a foundational overview of linguistic pattern analysis and how state-of-the-art LLMs (like GPT-4, Claude, PaLM 2, LLaMA 2, etc.) elevate it. We will explore real-world marketing and media applications – from sentiment analysis and content moderation to churn prediction and personalized marketing – and illustrate how these capabilities align with Sequence’s planned platform (as outlined in internal SMMAI documentation). Importantly, this is pre-project research: a rigorous concept study to inform future development, rather than results from a deployed system. The goal is to establish a shared understanding of the technology’s potential for Sequence’s stakeholders, setting the stage for upcoming AI-powered initiatives.

Understanding Linguistic Pattern Analysis

Linguistic pattern analysis is the systematic study of recurring structures in language to extract meaning and context. In practice, it involves examining how words and phrases are used across many documents or conversations to identify trends, themes, and subtle cues. Researchers have long used this approach in discourse analysis – for example, coding speech or text for patterns in syntax, semantics, and sentiment. By identifying repeated linguistic patterns, one can uncover hidden meanings and social dynamics that would otherwise remain buried in the noise. Essentially, language patterns act as signals; when decoded, they reveal how people express identity, emotion, or intent within communications.

In a marketing and media context, linguistic pattern analysis is highly relevant. Every brand mention, customer comment, or product review contains not just explicit feedback, but also implicit information embedded in tone, wording, and context. By meticulously analyzing these textual patterns, marketers can gauge audience sentiment, understand consumer motivations, and even predict behavior changes. For instance, certain phrases or emphases might correlate with positive engagement, while recurring complaints in support tickets might signal a product pain point. Pattern analysis thus turns unstructured language data into actionable insights – enriching communication strategies and decision-making. It bridges the gap between qualitative nuances in language and quantitative business outcomes.In short, mastering linguistic patterns helps organizations interpret the “voice of the customer” at scale and craft more resonant content and campaigns.

How LLMs Enhance Pattern Analysis

Traditional approaches to text analysis (like keyword tracking or classical machine learning models) have limitations in capturing context and nuance. Large Language Models (LLMs) fundamentally change the game. Trained on massive corpora of text, modern LLMs employ transformer-based neural networks that excel at understanding relationships between words in context .This means they don’t just count words – they genuinely grasp meaning and patterns across language on a human-like level. In fact, the rise of LLMs has upended conventional NLP techniques, as these models are able to capture complex linguistic and semantic patterns far more effectively by leveraging deep learning on vast data2. They recognize subtle cues like idioms, tone, and context shifts that older models or manual analyses often miss.

Leading LLMs today demonstrate remarkable pattern analysis capabilities:

OpenAI GPT-4: With over 100 billion parameters, GPT-4 is a state-of-the-art model known for its broad general knowledge and reasoning ability. It can interpret context, sentiment, and intent from text with high accuracy, often matching or exceeding human performance on language understanding tasks. GPT-4’s prowess in following complex instructions and its consistent output have made it a popular choice for everything from content analysis to generation. Moreover, OpenAI has shown that GPT-4 can be leveraged not just for understanding language but for applying guidelines – a property we’ll revisit in content moderation below.

Anthropic Claude: Claude is an LLM similar in capability to GPT-4, with a special strength – an extremely large context window. Recent versions of Claude can ingest up to 100k tokens (around 75,000 words) in a single prompt. In practical terms, Claude can read and analyze hundreds of pages of text at once and retain context over very long conversations. This enables deep pattern analysis across long documents or entire content archives. For example, Anthropic demonstrated Claude spotting a single edited line in an entire novel within seconds. Such capacity means Claude can synthesize insights from lengthy reports, books, or social media archives holistically – a powerful asset for media analysts dealing with extensive textual data. Claude is also designed with a focus on AI safety and reliability, which can be advantageous when analyzing content that requires nuanced judgment (e.g. distinguishing satire from hate speech).

Google PaLM 2: PaLM 2 is Google’s latest large language model (introduced in 2023) and is known for its strong multilingual and reasoning capabilities. Trained on text spanning over 100 languages, PaLM 2 can understand and translate nuanced expressions (idioms, cultural references, etc.) across a wide variety of languages. It also incorporates large volumes of technical and logical data (like scientific papers and math problems), giving it improved skills in logic and common-sense reasoning

. This makes PaLM 2 adept at analyzing language for structured insight – for example, parsing a complex customer complaint to discern not just sentiment but underlying causes that might be logically inferred. Google has deployed PaLM 2 across many products (over 25 features, from Gmail’s smart compose to Google’s coding tools), underlining its versatility. For global marketing initiatives, PaLM 2’s multilingual prowess means pattern analysis isn’t confined to English – it can just as effectively gauge trends in Spanish tweets or Mandarin reviews, making it invaluable for international reach.

Meta LLaMA 2: LLaMA 2 is an open-source LLM released by Meta (Facebook) in 2023, available freely for research and commercial use. Despite being open, it has demonstrated performance on par with top proprietary models. In internal evaluations, LLaMA 2 outperformed other open-source chat models on many benchmarks, including measures of helpfulness and safety 11 . It comes in parameter sizes ranging from 7B up to 70B, including a fine-tuned chat variant. The significance of LLaMA 2 is that companies like Sequence can host and customize it, integrating advanced language analysis without always relying on third-party APIs. Its strong benchmark results indicate it can handle sophisticated pattern recognition tasks, and its open availability allows for tailoring the model to specific domains (for instance, fine-tuning on marketing-specific data or industry jargon). Essentially, LLaMA 2 helps democratize LLM technology – enabling a wider range of organizations to embed AI-powered language analysisinto their platforms cost-effectively.

By leveraging these LLMs, linguistic pattern analysis becomes more powerful and efficient than ever. They maintain context, interpret nuance, and generalize from examples, which means they can detect patterns that humans or older algorithms might overlook. Whether it’s understanding that two differently worded reviews express the same complaint, or predicting how a subtle change in phrasing could alter audience perception, LLMs supply an unprecedented level of insight. The next sections examine concrete marketing and media applications where this proves valuable – all areas that Sequence Media & Marketing is eyeing for its platform.

Applications in Marketing and Media

Modern marketing is as much a data challenge as a creative one. The ability to analyze language at scale opens up a wealth of applications. Below, we discuss several high-impact use cases where linguistic pattern analysis with LLMs is transforming marketing and media. These examples also mirror the focus areas for Sequence’s upcoming AI-driven platform.

Sentiment Analysis and Social Listening

In marketing, sentiment analysis is key to understanding audience attitudes. Brands monitor public sentiment on social networks, forums, and reviews to gauge reputation and react to problems or wins. Traditional sentiment analysis tools (e.g. simple classifiers or keyword-based systems) can flag obvious positive or negative words, but they often misfire on sarcasm, context, or domain-specific language. LLMs significantly improve this process. Because they comprehend context and tone, large models like GPT-4 or PaLM 2 can classify sentiment with nuanced accuracy – even in tricky cases such as mixed feelings or idiomatic expressions.

Research has shown that LLMs can match or surpass specialized sentiment models without extensive task specific training. For example, one study found a GPT-3.5 model in zero-shot mode achieved sentiment accuracy on par with a fine-tuned BERT classifier. More impressively, an instruction-tuned LLM (ChatGPT based on GPT-4) outperformed dedicated models in analyzing open-ended survey responses, improving accuracy by several percentage points. In practice, companies are beginning to capitalize on this. Some customer service teams now use GPT-4 to interpret nuanced support ticket sentiment in real time, enabling quicker escalation of unhappy customers . The advantage is clear: an LLM can understand that a message like “I suppose this is fine…” (with an ellipsis) probably signals mild dissatisfaction, whereas a keyword-based analyzer might mistakenly mark it neutral. By catching subtle cues – word choice, punctuation, context of previous messages – LLM-driven sentiment analysis paints a more accurate picture of how customers feel.

From a social listening perspective, LLMs enable marketing teams to monitor brand mentions and conversations at scale. Rather than manually reading thousands of tweets, an LLM system can digest them and highlight emerging patterns: for instance, identifying that many users this week associate a new product with the word “expensive” or that excitement is spiking around a campaign in a particular region. Because these models have broad knowledge, they can even perform sentiment analysis in multiple languages or understand colloquial slang. This is hugely beneficial for a global brand tracking its reputation across diverse markets. Ultimately, sentiment analysis powered by LLMs gives businesses a kind of early warning system and performance barometer – if sentiment dips or a PR issue brews, the AI will surface it promptly, complete with the context needed to respond appropriately.

Content Moderation and Brand Safety

For any platform or community-facing brand, content moderation is a vital task to maintain a safe, positive environment. This includes filtering out hate speech, harassment, explicit content, or simply keeping discussions civil and on-topic. Historically, moderation has relied on large teams of human moderators plus some keyword matching algorithms – a combination that is costly, slow, and prone to inconsistency. LLMs offer a compelling solution by automating moderation decisions with consistency and context-sensitivity that far exceeds old keyword filters.

OpenAI’s use of GPT-4 for content moderation is a prime example. In their framework, GPT-4 is given the platform’s policy guidelines and then tasked with evaluating user-generated content against those rules. The results have been striking: policy updates that once took months to roll out to human moderators can now be implemented in hours . Why? Because the AI can instantly absorb a new or revised policy document (no lengthy training period needed) and start labeling content accordingly. GPT-4 is adept at interpreting the nuances in long policy documents and consistently applying them . For instance, it can understand subtle distinctions – a post containing violence in a news context versus one encouraging violent behavior – and label each appropriately per policy. This leads to more consistent moderation decisions across the board .

The efficiency gains are also enormous. A single LLM can process vast streams of content in real time, something even a large human team would struggle to do 24/7. OpenAI reported that an AI-assisted moderation system greatly reduces the burden on human moderators, who then only need to review edge cases or appeals . This not only improves moderator well-being (less exposure to toxic content) but also scales moderation to meet the volume of modern social platforms. For brands and media companies, brand safety is directly enhanced: LLMs can act as guardians of the brand’s online spaces, catching harmful content (e.g. racist remarks in a community forum, or spam in comments on an ad) before it spreads. The LLM can also be tuned to a company’s specific standards. For example, Sequence’s future platform could use an LLM to enforce each client’s unique content guidelines on their social pages, automatically hiding or flagging user posts that violate those standards.

It’s important to note that AI moderation isn’t perfect – models must be carefully tested for false positives/ negatives and biases. However, as an augment to human judgment, LLMs promise a moderation system that is fast, scalable, and adaptable. The outcome is a healthier digital environment and a safeguarded brand reputation, achieved with far greater speed and consistency than manual moderation alone.

Churn Prediction from Customer Feedback

Customer churn – the loss of customers or subscribers – is a critical metric for businesses to monitor and minimize. Traditionally, churn prediction models rely on structured data like usage frequency, purchase history, or customer demographics. While those are important, they miss a goldmine of insight contained in unstructured text: what customers are saying in support emails, cancellation reasons, product reviews, or on social media. Linguistic pattern analysis unlocks this resource. By mining textual communications, companies can identify early warning signs of dissatisfaction that precede churn.

Text-based churn prediction often starts with sentiment analysis on customer communications. If a long-time subscriber’s emails to support have increasingly negative tone, or their survey feedback grows more critical, these are red flags. LLMs can parse such feedback at scale and with nuance – discerning not just “negative or positive” but specific grievances or emotional cues. One powerful method is to detect hidden patterns: certain phrases or complaints that strongly correlate with eventual churn . For example, an insurance company might discover that when a customer uses language like “frustrated with the process” in correspondence, their likelihood of leaving soon spikes. An LLM can identify these patterns by examining thousands of past cases, finding linguistic markers humans might not isolate. By combining these qualitative signals with quantitative data, businesses can create more accurate predictive models. Recent industry discussions highlight how valuable this approach is. By analyzing support tickets, chat logs, and social media posts, organizations have been able to predict churn and intervene earlier . One report noted that mining unstructured text helps catch issues “before they escalate, potentially saving valuable customer relationships” . The key advantage is proactivity: rather than waiting for a customer to silently slip away, an LLM-driven system could alert a retention team that “Customer X has expressed severe frustration in the last two calls; they are at high risk of churn.” The business can then reach out with a targeted win-back offer or solution while there’s still time.

Consider how this could work on Sequence’s platform in the future. A company integrated with Sequence might feed in all their customer feedback data streams. The LLM analyzes this in near-real-time, classifying sentiment and highlighting churn signals. It might generate alerts like: “High churn risk detected: 28% of comments about Product Y in the past month mention 'not worth the money' – a significant upward trend.” Armed with that insight, the company can investigate Product Y, address pricing perceptions or quality issues, and directly contact the unhappy customers. In sum, by extracting sentiment, tone, and topic patterns from customer language , LLMs enable a form of churn prediction that is both broader (looking at all sources of feedback) and deeper (understanding the “why” behind dissatisfaction). This leads to more effective retention strategies and a stronger bottom line.

Personalized Marketing and Content Creation

One of the most exciting applications of LLMs in marketing is hyper-personalization– tailoring content and messaging to individual customers or finely segmented audiences at scale. Marketing has always understood that personalized messages (using a customer’s name, reflecting their interests) outperform generic blasts. But doing this manually or even with basic automation is labor-intensive and limited. LLMs, however, can analyze customer data and generate customized content on the fly, making true one-to-one marketing feasible and efficient.

Content creation is a domain where LLMs shine. These models can draft copy that captures a brand’s voice and resonates with target audiences. For example, an LLM could write dozens of variants of an email headline, each aimed at different customer segments (one emphasizing price for cost-conscious shoppers, another highlighting quality for premium buyers, etc.). In digital advertising, LLMs can generate ad copy tailored to specific audience segments, significantly improving relevance and engagement. Rather than one-size-fits-all ads, marketers can deploy a suite of AI-written ads each tuned to a demographic or even personalized to the individual level. According to marketing technology reports, this use of LLMs allows campaigns to dynamically optimizecontent: as real-time performance data comes in (clicks, conversions), the model can adjust the messaging to better appeal to the audience’s demonstrated preferences. This iterative optimization by the AI can boost click-through and conversion rates beyond what static content could achieve.

LLMs also bring huge time and cost savings in content production. Routine writing tasks – product descriptions, social media captions, basic blog posts – can be automated with AI assistance, freeing up human creatives to focus on strategy and high-level creative direction. For instance, an e-commerce retailer in a case study used an LLM to automatically generate product descriptions for thousands of items, ensuring each description was unique and on-brand while completing the task in a fraction of the time a human team would need . This not only slashed labor costs but also improved consistency across their site.

The personalization aspect truly comes alive when AI is fed individual customer data. Recommendation systems powered by LLMs can analyze a customer’s past behavior and language to suggest precisely the products or content they’re most likely to care about. Unlike traditional recommender algorithms that might use just purchase history, an LLM can incorporate subtler signals – say, notes from sales calls or the specific adjectives a customer used in reviews – to refine recommendations. Similarly, LLMs enable tailored marketing campaigns for segments : the model understands the language that resonates with, for example, environmentally conscious millennials versus budget-conscious retirees, and can help craft different messaging for each. As a result, the marketing content feels more relevant to each recipient, which translates into higher engagement.

There are already concrete success stories demonstrating the impact. Salesforce’s recent Marketing GPT initiative uses LLM technology to auto-generate personalized email content and even suggest smarter audience segments . Early adopters have reported more efficient campaign workflows and improved response metrics. In one published example, a travel agency applied an LLM to personalize their email campaigns – analyzing customer travel history and preferences, then generating tailored vacation suggestions for each subscriber. The result was a 25% increase in email open rates and a 15% increase in bookingscompared to their generic emails. Those are significant lifts in marketing performance, attributable to AI-driven personalization.

For Sequence Media & Marketing, these capabilities hint at a platform where clients could automate much of their content marketing with AI while still keeping it personal and on-brand. Imagine an interface where a user inputs a few branding guidelines and audience profiles, and the system generates a week’s worth of social media posts or ad copies, each tuned to different segments (and backed by data on what language appeals to those segments). The marketer becomes an editor/strategist, curating AI suggestions rather than writing everything from scratch. This synergy of human creativity and AI efficiency can lead to marketing campaigns that are both highly scalable and deeply personalized – a combination that drives stronger customer relationships and ROI.

Alignment with Sequence’s Platform Vision

The exploration above is not just theoretical to Sequence Media & Marketing – it directly informs the design of our future platform and services. According to our internal Sequence Media & Marketing AI (SMMAI) documentation, the planned platform capabilities center around intelligent content analysis, predictive insights, and automated engagement, all of which map closely to what advanced LLMs provide. By aligning LLM technology with these capabilities, Sequence aims to build a cutting-edge solution for clients in media and marketing.

Let’s highlight a few core pillars of Sequence’s platform vision and how LLM-driven linguistic pattern analysis underpins them:

Sentiment Tracking & Social Listening at Scale: Sequence’s platform is envisioned to continuously monitor social media and other channels for client mentions, campaign feedback, and industry trends. LLMs will be the engine that reads this firehose of text and surfaces what matters. The research shows that models like GPT-4 or PaLM 2 can accurately gauge sentiment and even complex reaction (e.g. detecting humor, sarcasm, or mixed feelings) in real time. This aligns perfectly with our goal of providing clients a live pulse on public opinion. Instead of dashboards cluttered with basic metrics, Sequence can deliver nuanced narratives: “Your new product launch is garnering enthusiastic responses in Europe (60% positive sentiment), with customers frequently praising the ‘ease of use’ – a phrase appearing in 40% of positive mentions this week.” Such insights, powered by LLM analysis, give businesses actionable understanding of audience perception that goes beyond raw counts of likes or mentions.

Automated Content Moderation & Quality Control: Whether it’s a brand’s community forum, a user-generated content campaign, or multi-channel ad placements, ensuring content aligns with brand values is a priority. Sequence’s platform will likely include moderation tools to filter out harmful content or flag PR risks in user comments. By integrating LLMs (as our research on GPT-4 suggests), Sequence can offer AI moderators that adapt to each client’s content policy. For example, an LLM could be fine-tuned on a company’s specific guidelines and then employed to review all incoming posts or ad submissions. It would consistently apply the rules – flagging a comment as harassment or an image caption as off-brand within seconds. The alignment with our platform is clear: we want to guarantee brand safety and compliance at scale, and LLMs are proven to excel at that with speed and consistency . This means clients can confidently engage in online campaigns or communities knowing an AI safety net is in place, reducing the need for manual oversight but with the option to have human review where needed.

Customer Retention Insights (Churn Prediction): A planned feature in the Sequence AI suite is advanced customer analytics – using AI to not only describe what customers did, but predict what they might do. One application is churn risk analysis. By channeling customer emails, chat transcripts, and survey responses through an LLM, Sequence can give clients an early heads-up on churn threats. Our research indicates that this approach catches qualitative signals of dissatisfaction that traditional metrics miss 22 23 . The platform could, for instance, provide a “Churn Risk Dashboard” where an AI summary notes trends like “25% of support chats this month include language indicating frustration with slow response times – up from 10% last month.” This would allow a manager to intervene (e.g., retrain support staff or improve response SLAs) before those frustrations turn into lost customers. Essentially, LLM-driven pattern analysis becomes an early warning and recommendation system within Sequence’s platform, aligning with the goal of proactive customer relationship management. By adopting LLMs, Sequence ensures that its churn prediction and retention tools are not just based on numbers, but also on the rich context of customer voices.

Personalized Content and Campaign Automation: At the heart of Sequence Media & Marketing’s vision is a platform that helps clients create and deliver content smarter. That means automation without sacrificing personalization. LLMs are the enabler here. As described, these models can generate tailored marketing content and even segment audiences based on language cues. Sequence plans to integrate content generation features (for social media posts, email drafts, ad copy suggestions) that users can leverage directly. The alignment with LLM capabilities is profound – our platform can incorporate models like GPT-4 or LLaMA 2 to serve as a creative assistant, proposing content variations optimized for different audiences or channels. Moreover, by analyzing which content pieces perform well (e.g., which version of a tagline got better engagement), the LLM can refine its suggestions over time, leading to continuous improvement of marketing effectiveness. This dynamic, AI-driven optimization of content and strategy is exactly what Sequence aims to offer as a competitive differentiator. We are essentially building on proven successes (like the Salesforce example or the travel agency case ) and bringing those capabilities to our clients in an integrated, easy-to-use platform.

Overall, the Sequence SMMAI initiative is about fusing these advanced AI capabilities into a cohesive product. The foundational research confirms that each capability – sentiment analysis, moderation, churn prediction, personalization – is not only technically feasible with current LLMs, but already demonstrated in real-world settings. This validation is important for our stakeholders. It means Sequence’s platform isn’t relying on sci-fi; it’s grounded in state-of-the-art AI that’s delivering value today. By aligning with the trajectories of companies like OpenAI, Anthropic, Google, and Meta (who are constantly improving LLM performance and accessibility), Sequence can ride the wave of innovation rather than reinvent the wheel. We plan to utilize a mix of best-in-class models (via API or open-source deployment) and fine-tune them where necessary to meet specific client needs.

In implementing LLMs, Sequence will also remain cognizant of data privacy and cost considerations – for instance, using open-source models (like LLaMA 2) on private data when appropriate, or employing efficient smaller models for certain tasks. The flexibility of having multiple LLM options allows our platform to be both powerful and adaptable. In sum, the research strongly guides our development roadmap: it tells us where LLMs can drive impact and ensures that Sequence’s upcoming platform features are built on a solid, validated foundation.

Conclusion

Linguistic pattern analysis powered by large language models represents a transformative leap for marketing and media analytics. As we've explored, the ability to automatically discern nuanced patterns in language – sentiments, intents, preferences, and risks – gives businesses a sharper understanding of their audiences and content than ever before. LLMs like GPT-4, Claude, PaLM 2, and LLaMA 2 act as sophisticated lenses, revealing insights hidden in the torrents of text that surround modern brands. They not only process language with human-like comprehension, but do so at superhuman speed and scale, enabling real-time responsiveness to customer attitudes and market trends.

For Sequence Media & Marketing, this foundational research underscores a clear opportunity: to embed LLM-driven intelligence at the core of our platform and services. By doing so, Sequence can empower clients to make data-driven decisions with greater confidence – whether it’s adjusting a campaign’s messaging after an AI analysis of social feedback, swiftly addressing a brewing PR issue flagged by AI moderators, or doubling down on a retention strategy because the AI identified positive shifts in customer sentiment. The alignment between what LLM technology offers and what Sequence’s platform aims to deliver is strong and by design. We’ve essentially charted the terrain of what’s possible with current AI, and used it to inform a roadmap that is ambitious yet realistic.

It’s important to reiterate that this study is conceptual and exploratory, laying groundwork for future case studies based on real implementations. No actual client data was used, and we haven’t rolled out these AI features in production – yet. Instead, we have gathered insights from academic research, industry use cases, and our own analyses to validate the concepts. This rigorous upfront work means that as we proceed to prototyping and implementation, we do so with a solid grasp of best practices and potential pitfalls. We are not chasing hype, but rather building capabilities grounded in proven technology.

Next steps will involve applied projects that bring these ideas to life. You can expect future case studies from Sequence that demonstrate pilot implementations – for example, a trial of GPT-4 for a client’s social media analysis, or the results of a churn prediction module tested on a sample dataset. Those will show quantitative outcomes and lessons learned from deployment. For now, the takeaway is that Sequence Media & Marketing is fully engaged with the cutting edge of AI for language analysis. We see immense potential in leveraging LLMs to enhance marketing outcomes, and our initial research strongly supports that direction.

By investing in this foundation, Sequence is preparing to deliver an AI-enhanced platform that is innovative, effective, and reliable. It’s a vision where technology augments marketing expertise: routine analysis is automated, insights are deeper, and content can be optimized continuously. Ultimately, that means better results for our clients – higher engagement, stronger customer loyalty, and more efficient campaigns – all achieved with the help of advanced AI understanding of linguistic patterns. We’re excited to move from concept to reality, confident that the alignment of LLM capabilities with our platform vision will create real value in the marketplace.

Sources: The insights and examples in this report are supported by current research and industry case studies. Key references include academic findings on LLM performance in sentiment tasks , OpenAI’s reports on using GPT-4 for content moderation, analyses of text mining for churn prediction , and documented successes of AI-driven personalization in marketing, among others. These sources demonstrate the state of the art in linguistic pattern analysis with AI, and they have been cited throughout the text for further reading and verification.

Twitter by the Numbers (2023): Stats, Demographics & Fun Facts

https://www.omnicoreagency.com/twitter-statistics/

Discourse Analysis Coding Techniques - Insight7 - AI Tool For Interview Analysis & Market Research

https://insight7.io/discourse-analysis-coding-techniques/

How to Use LLMs for Marketing Strategies | Pixis

https://pixis.ai/blog/llm-for-marketing/

Exploring the Potential of Linguistic Linked Data in the LLM Era | data.europa.eu https://data.europa.eu/en/news-events/events/exploring-potential-linguistic-linked-data-llm-era

Introducing 100K Context Windows \ Anthropic

https://www.anthropic.com/news/100k-context-windows

Google AI: What to know about the PaLM 2 large language model

https://blog.google/technology/ai/google-palm-2-ai-large-language-model/

Sociable: Meta releases advanced Llama 2 large language model to power new AI experiences |