{"id":207723,"date":"2025-12-30T21:10:27","date_gmt":"2025-12-30T21:10:27","guid":{"rendered":"https:\/\/www.similarweb.com\/blog\/?p=207723"},"modified":"2026-04-16T07:47:22","modified_gmt":"2026-04-16T07:47:22","slug":"fix-negative-brand-sentiment","status":"publish","type":"post","link":"https:\/\/www.similarweb.com\/blog\/marketing\/geo\/fix-negative-brand-sentiment\/","title":{"rendered":"How to Identify and Fix Negative AI Brand Sentiment"},"content":{"rendered":"

Artificial intelligence assistants and modern search engines now shape much of the public\u2019s first impression of a business. When a large language model describes your company, it can either reinforce trust or cast doubt.<\/p>\n

In a previous article, our SEO expert, Maayan Zohar, shared methods and best practices on\u00a0how to analyze AI brand sentiment<\/a> and understand these signals.\u00a0However, learning how to analyze sentiment is just the first step.<\/p>\n

The real question is how to identify when you have brand perception issues<\/b> (i.e, negative sentiment) and how to fix them.<\/p>\n

This article will teach you how to identify negative brand sentiment and, more importantly, how to fix it<\/b>. It combines research on AI sentiment analysis with real data from Similarweb\u2019s AI Brand Visibility tool<\/a> on the Avis brand to give examples of what to look for and where to intervene.<\/p>\n

What is AI Brand Sentiment?<\/h2>\n

AI Brand sentiment refers to how often a brand is described positively, neutrally, or negatively across AI\u2011generated answers.<\/p>\n

Generative engines’ responses are usually classified into three sentiment categories, yielding a \u201csentiment mix\u201d:<\/p>\n

\"AI<\/p>\n

The way that ChatGPT and other chatbots refer to your brand can set a potential customer\u2019s opinion for or against your brand. A positive description of your brand means more sales, while a negative description means losing potential sales.<\/p>\n

Understanding what causes your audience to have positive sentiment and trust in your brand, as well as why they have negative sentiment, is the basis of your ability to improve your brand perception in AI engines.<\/p>\n

This means that a critical part of your AEO<\/a> and GEO efforts should also be set for sentiment analysis and management.<\/p>\n

Since LLMs rely on their own data, along with external sources, reviews, and other user conversations when synthesizing their responses, those are the sources you\u2019d like to influence to showcase your brand positively.<\/p>\n

Let\u2019s start with understanding how LLMs \u201cunderstand\u201d sentiment. Leveraging this knowledge later on will help you trigger the correct sentimental interpretations from AI engines.<\/p>\n

How do LLMs classify negative or positive sentiment?<\/h2>\n

Large language models (LLMs) determine whether a text expresses positive or negative sentiment through a combination of learned language representations and a classification mechanism. The process typically involves the following steps:<\/p>\n

    \n
  1. Encoding the input text:<\/b> Each sentence is first converted into a numerical representation (a sequence of token embeddings).<\/li>\n
  2. Classification head:<\/b> A small feed-forward network on top of the language model produces a single score per sentiment class (positive, neutral, or negative). These scores are passed through a function to convert them into probabilities. The class with the highest probability<\/b>\u00a0is selected as the sentiment label.<\/li>\n
  3. Attention to context and nuances:<\/b> Unlike keyword\u2011based methods, LLMs analyze the entire sentence, capturing tone shifts, sarcasm, and context.<\/li>\n
  4. Tweaking and label output:<\/b> In a fine\u2011tuned classifier, the sentiment classes are fixed (e.g., positive\u202f=\u202f1, neutral\u202f=\u202f0, negative\u202f=\u202f-1).<\/li>\n
  5. Challenges and biases:<\/b> LLM\u2011based sentiment analysis is not flawless: subjectivity, ambiguous context, sarcasm, and idioms still present challenges. Early models sometimes showed a \u201cpositive bias,\u201d but newer instruction\u2011tuned LLMs like GPT\u20115 reduce this by calibrating their outputs against more balanced data.<\/li>\n<\/ol>\n

    LLMs don\u2019t \u201cfeel\u201d or hold opinions. They rely on mathematics to classify sentiment.<\/p>\n

    Even in zero\u2011shot settings, where you simply prompt a model to label sentiment, it isn\u2019t choosing based on preference, but predicting what someone might say<\/b> based on patterns<\/b> from its own knowledge graphs and training data.<\/p>\n

    The final choice of \u201cpositive,\u201d \u201cneutral,\u201d or \u201cnegative\u201d comes down to which option is mathematically most likely<\/b> given the input and the model\u2019s training, not any subjective feeling detection. This choice, eventually, is how your brand will be presented to the reader of the LLM-generated answer.<\/p>\n

    How to Tell if Your Brand Has Negative Sentiment Issues<\/h2>\n

    Spotting a sentiment problem requires more than just glancing at an overall score.<\/p>\n

    Negative feelings can stem from many sources: insufficient information, product flaws, pricing concerns, misinformation, or simply a lack of enthusiasm. To determine which of these issues you face, you need to interpret the data in context.<\/p>\n

    The framework below explains what each metric tells you, why it matters, and how to reason about the numbers. Each step uses the Avis<\/b> data to illustrate how an SEO should connect the dots between the raw figures and actionable insights.<\/p>\n

    1. Examine Your Sentiment Mix<\/h3>\n

    Begin with the ratio of positive, neutral, and negative mentions across all topics and channels. Think of this as a high\u2011level health check.<\/p>\n

    A healthy brand usually has a majority of positive mentions, a moderate neutral share (for users who are researching or comparing options), and a minimal negative share.<\/p>\n

    If negative answers consistently outweigh positive ones, you clearly have an issue. But even a small negative fraction can hurt when neutral sentiment is high because it signals that a large audience is still undecided or confused.<\/p>\n

    In the Avis<\/b> campaign, 1,076 mentions across ChatGPT responses were split into 37% positive, 61% neutral, and 2% negative<\/b>.<\/p>\n

    Checking Avis\u2019 AI Mode<\/b> visibility showed 3% negativity in responses vs 36% positivity (for 1,586 total mentions), and 0% negative sentiment in Perplexity<\/b> (807 mentions).<\/p>\n

    \"AI<\/p>\n

    This example will zero in on ChatGPT’s negative AI sentiment for Avis, despite AI Mode\u2019s 3%.<\/p>\n

    At first glance, negativity is very low, but that\u2019s no reason to ignore it.<\/p>\n

    You practice AEO and GEO<\/a> to get mentioned in AI answers. Leaving your hard-earned AI mentions<\/a> unmanaged can hinder your efforts and hurt your performance.<\/p>\n

    Every mention you earn should be tracked, preferably analyzed periodically, to make sure not only that your brand is mentioned, but that it\u2019s mentioned the way you want it to be<\/b>.<\/p>\n

    Now that you know the overall negative score, it\u2019s time to break it down by topics.<\/p>\n

    2. Break Sentiment Down by Topic<\/h3>\n

    Aggregate statistics mask problem areas. To find the fundamental drivers of neutral or negative sentiment, segment your data by topic, product line, or user intent. This helps you pinpoint which aspects of your offering confuse or disappoint people.<\/p>\n

    For Avis<\/b>, the topic Car\u202fRental Companies <\/b>has the strongest positive sentiment (63%), followed by Luxury and Specialty Car Rentals (38%). However, this analysis focuses on the negative sentiment.<\/p>\n

    Avis has negative sentiment in the topics Airport\u202fCar\u202fRentals (4%), Car\u202fRental\u202fCompanies (3%, despite the high positivity), and Van\u202f&\u202fTruck\u202fRentals (5%). Below is the full chart:<\/p>\n

    \"Check<\/p>\n

    What can cause negative AI sentiment toward Avis on airport car rental topics?<\/h4>\n

    This category had the highest absolute number of negative mentions.<\/p>\n

    Understanding what causes negativity in this category is easy: click the blue arrow next to the topic\u2019s name, then review the prompts and AI responses for that topic.<\/p>\n

    In this topic, most complaints centered on high fees, strict mileage policies, and limited vehicle availability<\/b>.<\/p>\n

    Addressing these concerns means being transparent about pricing<\/a>, simplifying fee structures, and ensuring sufficient fleet availability during peak times.<\/p>\n

    What can cause negative AI sentiment toward Avis on car rental companies topics?<\/h4>\n

    At the brand level, negative sentiment was relatively low (3%) but still notable.<\/p>\n

    Checking the topic\u2019s prompts showed users were experiencing confusion over which company offered the best value<\/b>, with occasional mentions of hidden costs or poor customer service<\/b>.<\/p>\n

    To reduce the negative sentiment surrounding car rental companies, Avis should clarify what distinguishes it from competitors, publish transparent pricing (including insurance and deposit policies), and highlight service\u2011quality guarantees.<\/p>\n

    What can cause negative AI sentiment toward Avis on van & truck rentals topics?<\/h4>\n

    This category had the highest negative share across topics (5%).<\/p>\n

    Many queries complained about mileage restrictions and questioned whether unlimited\u2011mileage options existed.<\/p>\n

    \"Analyze<\/p>\n

    Expanding mileage options, communicating them clearly, and ensuring competitive rates for larger vehicles can help move sentiment from negative to neutral or positive.<\/p>\n

    3. Read the Prompts Driving Each Sentiment<\/h3>\n

    Numbers alone don\u2019t explain why people feel a certain way.<\/p>\n

    To understand sentiment, you need to analyze the actual user prompts<\/a> or queries the model is responding to, as well as the response.<\/p>\n

    Negative prompts in the Avis<\/b> data highlight specific pain points:<\/p>\n