7 Of The Best Sentiment Analysis Tools For Product Teams

You and your customers are separated by a screen, leaving their true emotions hidden. 

The solution? Sentiment analysis tools.

But how do you pick the right one?

For all your questions on accuracy, data volume, multilingual capabilities, and more, here's a list to help you make an informed decision.

What is a sentiment analysis tool? 

A sentiment analysis tool is a software that analyzes texts to determine the sentiment or emotion behind them. 

It can help you classify the texts into positive, negative, and neutral. While sentiment analysis tools have different applications such as social media and surveys, they can help brands uncover insights to build better products. 

Tools that have better capabilities (read AI) can detect complex emotions including but not limited to such as sarcasm, humor, regret, gratitude, etc. for better insights. 

7 Sentiment Analysis Tools (that use AI)

1. Blitzllama 

Blitzllama is an AI-powered feedback platform with two core offerings:

• Pulse—a suite of rapid feedback collection tools like in-app surveys, link surveys, and widgets
• Eureka—AI-driven analytics to uncover insights from all customer data sources

Blitzllama has helped product teams in 200+ tech companies streamline their customer feedback processes while saving 40% of user research costs.

How does it fare?

Accuracy

Blitzllama uses fine-tuned BERT-based models for sentiment and emotion detection, which achieve 90%+ accuracy on most standardized datasets. 

As Blitzllama's models are fine-tuned on product data (reviews, survey responses, support conversations), they achieve 95%+ accuracy on sentiment analysis of product feedback.

Precision 

Blitzllama has UX guardrails to ensure the same user doesn’t participate in the survey before a specified limit. It helps avoid over-surveying and maintain precision.  

If you want to qualify visitor intent and personalize your customer journey, there’s a quiz that helps you do it. 

Scalability 

Blitzllama has native integrations with popular platforms such as Play Store and Intercom to continuously pull feedback data. 

Blitzllama processes more than 5 million pieces of feedback each month. Each piece of feedback gets tagged with sentiment within a few minutes.

Multilingual capability

Blitzllama can translate insights into 14 languages including Arabic, Spanish, Portuguese, German, and French.

Comprehensive Data Sources 

Blitzllama can help analyze feedback across various sources including but not limited to surveys, support tickets, and Play/App Store reviews. 

It also has a CSV option to upload data from other tools such as CleverTap and Mixpanel to help you uncover sentiments behind the user feedback.

Analysis Depth 

i. Sentiment Analysis

Blitzllama excels in not just categorizing feedback by sentiment but also in identifying the user's intent. Whether it’s a complaint, a request for help, a suggestion for improvement, or words of praise, Blitzllama captures the full range of user intent and emotions. 

This comprehensive approach offers businesses a deeper, more nuanced understanding of customer feedback.

ii. Custom Topic Categorization

One of Blitzllama’s standout features is its ability to automatically sort feedback into custom topics. 

By creating a flexible and personalized taxonomy tailored to the specific needs of each company, Blitzllama helps businesses uncover topic-level insights that are crucial for prioritizing actions and driving impactful improvements in products and services.

iii. AI-Powered Search

Blitzllama’s advanced semantic search functionality enables businesses to find contextually relevant insights across all their feedback data.

With the ability to auto-summarize results and save searches, companies can quickly access the critical information needed to make informed decisions.

iv. Automated Reports 

Blitzllama streamlines the reporting process with its automated report templates, allowing businesses to easily discover revenue opportunities, pinpoint top customer pain points, and gain insights into the customer journey. 

These reports empower companies to make strategic, data-driven decisions that enhance customer satisfaction and boost overall business performance.

Ease of Use

Customers frequently point out Blitzllama's interface as intuitive and easy to use.

Pricing 

Blitzllama offers a 30-day free pilot which allows you to experience all the features. 

After the completion of the free pilot, Blitzllama offers a growth plan that starts at $75/month and scales based on your consumption that month. 

Such usage-based pricing is more aligned with the value generated for you compared to fixed subscription plans.

2. Brand24  

Brand24 is an AI-based sentiment analysis tool that covers social media, blogs, news, videos, forums, podcasts, and reviews. It isn’t just limited to this. It can monitor sentiments across 25,000,000+ online avenues in real-time. 

How does it fare? 

Accuracy 

While Brand24 does a good job as a sentiment analysis software, it has its cons. This includes monitoring irrelevant reviews that have nothing to do with your brand. It falls short of filtering spam and junk content. 

Overall, they have achieved a sentiment analysis accuracy score from 61% to 95%. 

Precision 

When it comes to precision, there are mixed opinions. For the most part, Brand 24 helps identify sentiments, and find what they are looking for—relevant features and its benefits.

Another part that it could improve is its ability to gauge emphasis, attitudes, and colloquial terms. More importantly, false negatives. 

Scalability

Brand24 does a good job with handling large volumes of data but it doesn’t have a cap when the quota exceeds. This is a concerning issue that could hurt small and early-stage companies. 

Plus, there’s an extra charge for capturing historical data. 

Multilingual capability

Brand24 offers sentiment analysis for 90+ languages but may need more improvements in Polish, Malay, and Spanish.

Analysis Depth

Emotion Detection: Brand 24 offers deeper emotion detection such as admiration, anger, joy, disgust, fear, sadness, and neutral. 

Sentiment Analysis: The sentiment analysis tool categorizes the sentiments into positive, negative, and neutral. 

Data visualizations: The tools help visualize sentiments over time through pie charts and plot charts. 

Real-time insights: The tool like many others offers real-time insights from the majority of sources except surveys. 

Competitor analysis: Brand24 offers real-time insights into competitors. The automated reporting summarizes the entire strategy for easy perusal. 

Topic Identification: Brand 24 can identify and group similar topics that are relevant to a user audience.

Ease of use

The tool is largely fair to use but poses problems while setting up integrations, linking to Meta platforms, and filtering out a source without muting the entire channel. 

Pricing Model

The monthly pricing model starts at $149(individual) to $199 for the team plan. The pro plan and the Enterprise plan are $299 and $499 for the Pro and Enterprise plans respectively. 

3. Dovetail 

Dovetail is a user research and analysis tool that offers sentiment analysis. It is used by UX researchers, product designers, product managers, customer success managers, data analysts, software developers, and academic researchers

It is also known for thematic analysis summarizing product feedback in seconds. 

How does it fare?

Accuracy 

Dovetail’s accuracy is evident due to its ability to dig up hidden patterns from interviews, and survey feedback. Plus, it can recognize the sentiment behind it.

It helps you save 38+ hours per week by identifying repetitive patterns from different sources of feedback. 

This sentiment analysis tool can convert raw data into functional insights in just a few minutes by eliminating human error. The AI model is continuously updated so that it evolves as time passes by. 

One key thing customers love about Dovetail is its ability to create an automated transcription of videos so customers don’t have to watch the video multiple times. 

Precision 

Dovetail is known for focusing on critical areas such as pain points and new feature requests. While visualizing data on charts is decent, many users have expressed the need to go deeper into tags that occur together. 

By allowing users to group and classify insights, it allows users to focus on solving problems by eliminating clutter. 

Scalability 

Dovetail can provide insights even for large data sets. It uses cloud infrastructure such as AWS for data storage and management. This makes it easier to handle huge volumes of data. 

Multilingual capability 

Dovetail can transcribe over 40 languages. However, some users have complained about the quality of transcription in Spanish and Portuguese. 

Data sources 

Dovetail can collect feedback from videos, product feedback surveys, sales demos, voice of the customer sources, etc 

Analysis Depth

Emotion detection: Like most AI sentiment analysis tools, Dovetail can further categorize positive and negative sentiments into groups such as angry, annoyed, aggressive, happy, pleased, satisfied, etc. 

Topic Identification: Dovetail allows users to tag topics and create labels to build a taxonomy. This allows hierarchy and structure ensuring consistency thanks to themes. 

It mandates standardization of themes and topics for better collaboration among teams. 

Charts and Reports: Dovetail offers charts and reports that help users create charts based on highlight tags, note tags, and tagged text volume. It goes a step further by offering chart filters by note tags and fields.

Actionable Insights: Dovetail just like its competitors helps users find meaningful insights from complex data. Something that product managers, designers, sales, and researchers love.

Ease of use

‍Dovetail is largely user friendly but it does need to make clipping videos and managing data easier. 

Pricing 

Dovetail offers a free plan, professional plan at $39 per month with custom pricing for enterprises. 

4. Enterpret 

Entrepret is a feedback-unifying tool that centralizes all kinds of data in one place. It can help you solve the most important customer problems for high returns. 

And, yes, it unifies feedback channels such as Zendesk, Slack, NPS surveys, X, app store reviews, and communities. 

How does it fare?

Accuracy 

Entrepret uses NLP and ML models to analyze sentiments accurately. While it does its job well, it's striving to improve and get it accurately. It is always training its models to become highly accurate and give precise insights. 

Precision 

With its Quantify feature, Entrepret allows you to narrow down negative feedback, and quickly spot trends and anomalies, while discovering hidden insights for better prioritization. It is known for its ability to extract actionable insights from customer reviews and support calls. 

Scalability 

Enterpret can handle large volumes of data without any issues. It has a cloud-based serverless architecture in place that helps in auto-scaling large amounts of data. 

Multilingual ability 

Enterpret can analyze sentiments in multiple languages such as English, Spanish, French, German, Italian, and Portuguese. While the language precision may differ, it has strived to make non-english transcriptions clearer.  

One good thing about Enterpret is that it uses local language data sets to make it precise.

Data Sources

Entrepret can extract text data from a wide range of sources including social media, reviews, surveys, emails, chat transcripts, news articles, and more. 

It uses connectors to pull data from Twitter, Facebook, Google, Amazon, Yelp, etc. The platform can also integrate with internal data sources via APIs.

Analysis Depth

Along with basic sentiment scoring, Entrepret provides deeper analysis capabilities:

• Emotion detection to identify emotions like joy, anger, sadness, etc.
• Aspect-based analysis to detect sentiment toward specific product features or topics
• Fine-grained scoring on a scale from 1-5 or 1-10
• Trend analysis to spot sentiment shifts over time
• Detailed reports with charts, insights, and recommendations
• The key thing that stands out is the ability to filter users by ARR 

Ease of Use

Entrepret is user-friendly too making it easy to set up projects, configure data sources, run analysis, and view results. It provides pre-built templates for common use cases. The platform also offers APIs for programmatic access and integration with other systems.

Pricing 

Entrepret offers custom plans for users with perks such as customer-specific ML models, data auditors, unlimited user seats, and a 3-month data backfill. 

5. Sprinklr 

Sprinklr is a customer experience management tool that has broader software products comprising conversational tools, social media monitoring, and marketing automation. 

It’s typically used by large organizations because of its pricing and diverse tools. The personas who use it the most are social media managers, marketing managers, customer service representatives, product managers, etc. 

How does it fare?

Accuracy 

Sprinklr as a sentiment analysis software uses a model that is trained in over a million manually annotated messages spanning 20 industries. 

It makes 10 billion predictions per day with an accuracy of more than 80%. All of this is in 100+ languages. 

Scalability

Sprinklr can collect feedback from structured data from 25+ social media websites, 350 million web sources, internal data, surveys, and call transcripts. All of this is 100% analyzed using social conversations and calls. 

Multilingual ability 

Sprinklr supports sentiment analysis in 100+ languages with 80% accuracy. It has a couple of languages that aren’t supported by other tools such as Yiddish, Inuktitut, Icelandic, and Afrikaans. 

Data Sources

Sprinklr can analyze data from various social media sites, review sites, forums, blogs, and new sites. Adding on, it can analyze data from customer support channels(call log, email) surveys/feedback, Play Store, Youtube, and website comments.

Analysis Depth

• Sprinklr offers intent-based, emotion-detection, aspect-based, and fine-grained sentiment analysis
• It can detect emotions like happiness, frustration, disappointment
• The aspect-based analysis identifies sentiment tied to specific product features
• Fine-grained analysis classifies sentiment into highly negative to highly positive
• Sprinklr generates a CSAT score for every customer interaction—it can be combined with NPS and CES 
• It might be challenging for smaller teams with simple use cases plus is expensive
• Integration might be challenging 
• It provides charts, reports, and actionable insights to optimize customer experience

Ease of Use 

Sprinklr’s popularity is because of its user-friendly interface. However, recent reviews on G2 suggest that the academy courses haven’t been updated. This could present a learning curve for newbies. 

Depending on the use case, it can be far from DIY. It needs constant customer support access and bugs that need to be fixed. As earlier mentioned it's expensive, you can go for a better tool unless you need a highly custom-built tool.

Pricing

Sprinklr has four pricing plans namely service, social, insights, and marketing. Each has different pricing which needs to be individually evaluated.

6. Chattermill 

Chattermill is a customer feedback analytics tool that unifies data from social media reviews, support tickets, chat, and sales conversations. 

Like most tools in this list, it uses AI to declutter the insights from the noise. It finds takers from product, customer support, and marketing teams. 

How does it fare?

Accuracy 

Chattermill allows users to extract themes and insights without having to read feedback one by one. It is highly accurate in drawing comparisons between sentiments over a period say 3-6 months.  

It does all of this with the help of Lyra, its proprietary AI model. It can analyze multiple sentiments from a single feedback without any duplicates. Even after multiple analyses, the results remain the same.  

Moreover, it gives you granular insights while handling large datasets. 

Precision

Chattermill offers correct insights by building custom theme structures and AI models unique to each business and industry. This helps in analyzing contextually relevant sentiments. 

It has AI models trained on high-quality customer data from 50+ channels.

Scalability 

With customer data from 50+ channels, it centralizes feedback from surveys, product reviews, support tickets, and social media. With its aspect-based sentiment analysis, it can calculate the pain points, feelings, emotions, patterns, and trends from 1000s of data points every month. 

It can generate GPT summaries of support tickets and lengthy email threads. 

Multilingual capability 

Chattermill can translate in 100+ languages and does a great job except translating phonetic spellings. It means it can’t translate spellings based on how they sound. It can do so on the correct spellings. So, if there’s a typo, it might misinterpret it. 

Comprehensive data sources 

Chattermill can extract data from 50+ channels comprising of:

• CRM systems 
• Customer support platforms 
• Playstore/Appstore reviews 
• Online surveys 
• CEM systems

Analysis Depth

Users typically have a positive view thanks to its user-friendly interface. While it does a fairly good job by analyzing scattered feedback, users have suggested accuracy problems in Spanish which is a huge downside if you’re serving the Latin American markets. 

Other problems that have been highlighted are inaccurate tagging and difficulty in removing old users. It also doesn’t accommodate the needs of fast-growing startups, especially the structure of themes and categories.  

More importantly, some users expected a cloud visualization to help classify problems. 

Ease of Use

While Chattermill has been praised for its ease of use, there are complaints such as the issue of setting it up and deleting older dashboards.  

Pricing 

Chattermill offers 3 pricing plans that have custom pricing including pro, team, and enterprise. Contact them for more details.  

7. ChatGPT, Gemini, & Claude 

Using AI tools for sentiment analysis is a common practice. But should you use them? 

Well, ChatGPT, Gemini, and Claude have brilliant use cases but they each have their limitations. 

Pros of ChatGPT/Claude/Gemini compared to Blitzllama:

a. High accuracy

ChatGPT, Claude, and Gemini are state-of-the-art models designed for complex tasks, potentially offering high accuracy in sentiment analysis. 

The latest models have an accuracy of 92+%; with ChatGPT at 95%. However, you might want to note that the analysis ability of ChatGPT has relatively low accuracy.

b. Multilingual support 

The AI models are multilingual, allowing for sentiment analysis across various languages and expanding the scope of their applicability. 

ChatGPT and Gemini support over 50+ and 40+ languages respectively. Official docs of Claude mention support for only 4 languages - English, Japanese, Spanish, and French. 

c. Easy to use 

With their chat-based interfaces, these tools are incredibly user-friendly, especially for those in the tech sector who are likely already familiar with them.

d. Practically free 

For limited use, these AI models are practically free, making them accessible to a wide range of users and organizations.

Cons of ChatGPT/Claude/Gemini compared to Blitzllama

a. Limited context windows 

The context window limits of these AI models can restrict their ability to analyze large-scale datasets efficiently. 

Each tool has a context window of at least 32k (24k words), but accuracy decreases and hallucinations increase with context lengths > 5k i.e. 200 reviews.

b. Limited analysis capability 

They offer limited analysis capabilities, lacking features such as topic clustering and filtering options necessary for in-depth sentiment analysis.

c. Disconnected from workflows

These AI models are typically disconnected from other tools in your workflow, necessitating manual intervention that can slow down analysis and update processes.

d. No dashboards or charts

Unlike specialized sentiment analysis tools, these AI models don't provide dashboards or charts for real-time reference, nor do they offer automated trend or anomaly detection.

e. No automated alerts or reports

They cannot generate and send automated reports to your email or Slack, which can be a significant drawback for teams requiring regular updates.

Sentiment Analysis Tools FAQs 

1. How accurate is AI sentiment analysis?  

The accuracy of AI sentiment analysis software is dependent on the algorithms used, the quality of training data, and the intricacy of the language analyzed.  

AI models and tools trained on GPT-4 perform better in analyzing complex emotions such as despair, regret, etc.  

While most tools have a 70-80% accuracy, you might also want to consider the understanding of the industry jargon. For best results, use AI sentiment analysis tools that train models on your industry data, short vs. long context, and update models every few months. 

2. Which technique is required for sentiment analysis? 

Common techniques include machine learning (e.g., Naive Bayes, SVM), deep learning (e.g., LSTM, BERT), and lexicon-based approaches. 

The choice depends on the specific use case, data availability, and desired accuracy. For instance, BERT (Bidirectional Encoder Representations from Transformers) is highly effective for understanding the context of social media posts. 

While a simple lexicon-based approach might suffice for analyzing customer feedback forms.

3. What ROI can we expect from investing in a sentiment analysis tool? 

ROI varies widely but can be significant. Benefits include improved customer satisfaction, better product development, and more effective marketing. 

Some companies report 10-30% increases in customer retention or sales conversion rates. For example, Expedia used sentiment analysis to improve its website's user experience, resulting in a 5% increase in bookings, translating to millions in additional revenue.

4. How does sentiment analysis align with our product strategy and business goals?

Sentiment analysis can help create a tactical product strategy and realistic business goals by: 

Prioritizing: It can identify friction points, drop-offs, identify bugs, and evaluate if a new feature request meets product goals—aligning with the product roadmap.  

Enhance brand messaging: By identifying the reasons why customers buy from you, you create better messaging targeting your ICP. It can help address whom your product isn’t for.  

Improve customer support: Based on the customer feedback, you identify weak points and train customer support better, creating better help documentation. It can also help you gauge customer sentiments about competitors and take measures to improve. 

5. How to calculate sentiment score?

Sentiment scores are typically calculated on a scale (e.g., -1 to 1 or 0 to 100), where negative numbers or lower scores indicate negative sentiment, and positive numbers or higher scores indicate positive sentiment. 

The exact calculation method varies by tool and approach used. For example, Amazon Comprehend uses a scale of 0 to 1, where scores below 0.4 are considered negative, above 0.6 are positive, and between 0.4 and 0.6 are neutral. 

A review stating "This product is fantastic!" might receive a score of 0.9.