What sentiment analysis tools to use for your LLM App?

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When building an LLM app you have to understand what’s happening inside your users’ minds as much as you need to understand what’s happening inside the app itself. Analytics tools have evolved from simple data spreadsheets to highly specialised sentiment analysis tools to really track and understand how users feel and behave with your app.

With the speed at which products can be built with the help of AI and no code tools, it’s imperative to iterate quickly as well. Product development today necessitates thorough LLM sentiment analysis to obtain richer, more actionable user data in order to inform truly effective iteration cycles. It’s by iterating faster towards your customers’ desired user experiences than your competitors that let’s you take a bigger share of the market.

In this article we will cover the 3 best sentiment analysis tools and, more importantly, how they can be leveraged to improve and optimise your LLM app.

The Importance of Sentiment Analysis in LLM Apps

In a nutshell, sentiment analysis is a process of determining how your users feel about your product by analysing pieces of text when they interact with it.

But not all users will make it clear whether they are satisfied or unsatisfied with your LLM app, which is why this subset of product data is required to paint a picture of their overall satisfaction.

Therefore, the real importance of LLM sentiment analysis tools is that it automates the tracking of customer satisfaction and turns unstructured data into actionable insights that help with:

• streamlined, real-time feedback analysis
• better problem resolution
• improved product quality

This is your leverage for faster, more effective iteration towards the LLM app your target market really needs, so the better the data gathering and insights, the better the product.

However, this is not an easy task to undertake without the right tools to properly track, sort and analyse data.

So before taking an in depth dive into each LLM sentiment analysis tool in this article, let’s look at a quick TLDR overview of the three we’ll be comparing:

TLDR Overview of Top 3 Sentiment Analysis Tools for LLM Apps

1) Brand24 (Detailed)

Brand24 monitors, tracks, and analyses your LLM app mentions or topics related to your business from multiple different sources: social media, review platforms, blogs, forums, podcasts, websites and newsletters. It collects data for app feedback in real-time and offers arguably the best sentiment analysis across media with their advanced natural language processing (NLP). However, although Brand24’s robust analytics can be well adapted for LLM apps, it’s primary use case is for brands analysing sentiment across online media.

2) MonkeyLearn (User-friendly)

A smaller, more get up and go text analytics option for LLM sentiment analysis. What’s great about MonkeyLearn is its quick setup and easily customisable models with clear data visualisations for real-time processing. They also offer an open source API and third party plugins which makes it ideal for small, early stage startups looking for flexibility as well as insightful reporting and statistics. They were acquired by Medallia in February 2022 but still operate under the same name.

3) Phospho (Recommended for LLM apps)

Phospho is an open-source text analytics platform specifically designed for improving LLM apps with speed and ease of use in mind. Phospho enables companies to log and extract real-time insights from user interactions, evaluate models and sentiment continuously, and streamline the iteration cycle of their GenAI products with better informed product decisions. Phospho is very simple to integrate into your existing tech stack, a couple lines of code are enough to help you start analysing unstructured data to gain visibility into user sentiment.

Why these sentiment analysis tools?

The criteria for our choice of LLM sentiment analysis tools for your LLM app prioritise speed, ease of use and integrations, and obviously accuracy so that you have a good balance between data quality and flexibility to start leveraging rich insights quickly.

Integrations are an important factor because the market for sentiment analysis tools has not kept pace with the development of AI, leaving little options for tools specialising in AI integrated apps. That’s why we’ve included Phospho, a data analytics platform specifically made for LLM apps. Both Brand24 and MonkeyLearn are also included for their balance in flexibility, overall quality, and ease of integration.

All 3 tools can be used for real-time sentiment analysis so for a more informed decision, let’s compare their differentiating features and best use cases.

Tool 1: Brand24

Natural Language Processing (NLP)

Advanced sentiment analysis from NLP detects more specific emotions: admiration, anger, disgust, fear, joy, and sadness.

AI Assistant

This is like having a personal assistant who knows everything about your brand’s sentiment, performance, competition, and audience. It operates similarly to Chat GPT but has an edge: it can access your product data. Brand24’s AI assistant uses advanced language models to transform your product related questions into data driven insights.

Anomaly Detector

In short, it constantly watches your project to discover any anomaly that could impact your reputation and sentiment. For example, suppose your product suddenly receives significantly more mentions than usual, in that case, the Anomaly Detector will notice it, search the web for the most likely reason, and present its findings in one or two key sentences. You can also get customised alerts so you’re the first to discover changes in the volume of different sentiment around your product.

Use case: Social Media Monitoring (around your LLM app)

The best use case for Brand24’s analytics capabilities are in overall reputation visibility of your LLM app across online media with real-time custom alerts and trend detection in your product’s mentions. Another helpful angle to this use case is the marketing component from the potential to find the best influencers or affiliates with minimal effort by identifying the people talking about your product across any social platform.

Pricing

Brand24’s pricing starts at $119/month for the ‘Individual’ plan, but for most startups and teams building LLM apps the ‘Team’ plan which starts at $159/month is the most suitable to get started. They do offer more premium plans at $239 and $399 per month which you can switch to as you scale.

Tool 2: MonkeyLearn

Custom tags (easy customisation)

All you have to do is create categorisation tags and then manually highlight different text parts to show what content belongs to each tag. Over time, the software learns on its own and can process multiple files simultaneously.

Data Visualisation Templates (quick start)

With a range of templates made for different scenarios with pre-made text analysis models and dashboards, the process is simplified and allows you to upload data, run the analysis, and instantly visualise actionable insights. Their instant data visualisations also make understanding your unstructured data quickly really easy.

Open source API & third party plugins

Fast moving teams will really benefit from their easy integrations. Whether it’s using their API to seamlessly connect sentiment analysis into your tech stacks, or plugging popular apps such as ZenDesk and Google sheets into MonkeyLearn’s platform with native integrations and APIs.

Use case: Custom Model Training

Leaning into MonkeyLearn’s easier integration and customisation capabilities, you can fine tune your AI model for your LLM app with tailored sentiment analysis based on app-specific jargon or features. By integrating directly into your LLM with their API you can monitor and evaluate in-app user interactions to determine sentiment around new features and collect live feedback for better prioritisation in future development cycles.

Pricing

There is a free plan for academic use but MonkeyLearn’s standard pricing start’s at $299/month with more than enough functionality for startups building LLM apps. But for custom usage limits they have a business plan where you would have to contact them for a quote based on your specific needs.

Tool 3: Phospho - A Unique Approach to Sentiment Analysis for AI-Powered Products

Reports now suggest that 70% of new apps in 2024 integrate AI LLMs, which shifts the need for AI specific analytics tools into more pressing focus. This is why we built Phospho to provide more capable and focused AI product analytics specifically for LLM apps.

With this positioning most practical use cases are more fitting and specific to the needs of startups building LLM apps:

Feedback loop enhancement

With real time monitoring to continuously capture user interactions, you can have immediate sentiment feedback at a glance with visualisations on your dashboard. This allows you to quickly identify and respond to any unexpected shifts in sentiment, making sure your LLM app is responsive to what your users need.

This is reinforced with custom KPIs which allow you to fine tune your monitoring for specific edge cases, different sentiments, or user segments. By gathering and logging more diverse feedback data to operate with, you can have more control and visibility over your LLM app’s performance.

Phospho lets you label, tag and annotate any of these logged interactions so you can train and fine tune your model to produce more refined, natural user experiences over time.

AI Product Development

Rich insights into how your users feel about product decisions can inform better future development. For example, leveraging sentiment trends across different features, or amongst certain user segments can help you understand which features to prioritise and whether they will resonate with your users.

The ability to do this in real-time with flexibility and customisation is a huge competitive advantage for AI product development to stay agile, where speed and effectiveness can be deciding factors in a saturated market.

Customer support optimisation

With our custom KPIs and flagging, you can configure alerts to signal when Phospho automatically detects a certain sentiment you have defined. In this way you can respond to negative interactions in real time (manually or automatically), thereby improving user satisfaction and retention.

Pricing

Our pricing at Phospho reflects the accessibility we emulate with our approach to the platform itself. We want any teams building with AI LLMs to leverage the potential for these insights. As such, we offer a free trial with $10 (10k analysis) of free credits for up to 15 team members. Thereafter, we have a standard $1/1k analysis tier and a custom tier based on your needs so feel free to either contact our sales directly or sign up from here.

While all three tools are valuable, Phospho’s unique approach and features make it particularly well suited for AI driven products looking to leverage sentiment analysis for continuous improvement.

Choosing the Right Sentiment Analysis Tool for Your LLM App

When choosing the right sentiment analysis tools, consider weighing up your available resources against what you require as standard expectations first, and what sort of features or data sources meet your specific needs.

If you’re building a simple app or brand online, Brand24 or MonkeyLearn’s intuitive and online media specific positioning makes it a suitable option.

However, if you’re building an AI integrated product such as an LLM app and require a simple get up and go solution with flexibility in what data you track to optimise your model, Phospho makes the most sensible choice for LLM sentiment analysis.

It’s ability with identifying edge cases, customisable data sources, and easy integrations with LLMs and tech stacks offer an all round solution for startup needing cost effective and robust product analytics.

Understanding customer sentiment and feedback whether it’s direct or indirect is more advantageous than ever in the AI product development space.

Get started for free and integrate Phospho into your LLM app to see the benefits of sentiment analysis by signing up here.