Monday, April 22, 2024

7 Best Sentiment Analysis Tools for Growth in 2024

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Sentiment analysis tools use artificial intelligence and deep learning techniques to decode the overall sentiment, opinion, or emotional tone behind textual data such as social media content, online reviews, survey responses, or blogs. The best sentiment analysis tools accurately and granularly analyze sentiments behind the text, employing a hybrid approach of statistical and knowledge techniques to help organizations understand customer opinions, market trends, and brand perceptions across platforms.

We reviewed the top sentiment analysis tools for businesses and organizations to see how they compare on features, pricing, and key enterprise requirements to help you find the best option. Here are our picks for the top sentiment analysis tools and software to drive growth in 2024:

Best Sentiment Analysis Tool Comparison

The following table provides an at-a-glance summary of the essential features and pricing plans of the top sentiment analysis tools. All prices are per-user with a one-year commitment, unless otherwise noted.

Core Features Number of Supported Languages and/or Dialects Key Differentiators Pricing
IBM Watson NLU
  • Domain customization
  • Metadata
  • Semantic roles
13 Backed by IBM Watson Starts at $0.0002 per NLU item, per month
Azure AI Language
  • Summarization
  • Key phrase extraction
  • Custom text classification
100+ Powered by Z-Code++ and Azure OpenAI Free to $25 per 1,000 text record
Talkwalker
  • Social reporting
  • Follower analysis
  • Post performance tracking
187 Extensive international coverage for social conversations Contact sales for quote
SAP HANA Sentiment Analysis
  • Text mining
  • Data preparation
  • Multi-model engines
31 Supported by SAP ecosystem Starts at $9.84 per capacity unit
MonkeyLearn
  • Workflow automation
  • Direct integrations
  • Classifiers and extractors
19+ Text analysis integration in business workflows Starts at $299 per month
Meltwater
  • Media database
  • PR insight reports
  • AI insight explainer
242 Heavy focus on multimedia monitoring Contact sales for quote
Idiomatic
  • Customer intelligence
  • Custom data labels
  • Sentiment analysis models
50+ Deploys channel-specific sentiment models Starts at $399/month per data source

IBM icon.

IBM Watson NLU

Best Overall Sentiment Analysis Tool

Overall Rating: 4.3/5

  • Core Features: 4.7/5
  • Advanced Features: 5/5
  • Cost: 4/5
  • Ease of Use: 3.5/5
  • Customization & Integrations: 4.1/5
  • Customer Support: 3.5/5

IBM Watson Natural Language Understanding (NLU) is an AI service for advanced text analytics that leverages deep learning to extract meaning and valuable insights from unstructured data. It can support up to 13 languages and extract metadata from texts, including entities, keywords, categories, sentiments, relationships, and syntax. Users can train a model using IBM Watson Knowledge Studio to understand the language of their business and generate customized and real-time insights.

IBM Watson NLU interface.
An example of sentiment scoring in a media text using the IBM Watson NLU.

Product Design

IBM Watson NLU has an easy-to-use dashboard that lets you extract, classify, and customize text for sentiment analysis. You can copy the text you want to analyze in the text box, and words can be automatically color-coded for positive, negative, and neutral entities. In the dashboards, text is classified and given sentiment scores per entity and keyword. You can also easily navigate through the different emotions behind a text or categorize them based on predefined and custom criteria.

Product Development

IBM Watson NLU recently announced the general availability of a new single-label text classification capability. This new feature extends language support and enhances training data customization, suited for building a custom sentiment classifier. Once the model is trained, it will be automatically deployed on the NLU platform and can be used for analyzing calls.

Why We Picked IBM Watson NLU

IBM Watson NLU stands out in terms of flexibility and customization within a larger data ecosystem. Users can extract data from large volumes of unstructured data, and its built-in sentiment analysis tools can be used to analyze nuances within industry jargon. Its deep learning capabilities are also robust, making it a powerful option for businesses needing to analyze sentiments from niche datasets or integrate this data into a larger AI solution.

Pros and Cons

Pros Cons
Seamlessly integrates with IBM products Set up requires more technical expertise
Flush with advanced data analysis tools Casual business users might not harness its full potential

Pricing

Pricing is based on NLU items, which measure API usage and are equivalent to one text unit, or up to 10,000 characters. One NLU item is analyzed for one feature, such as sentiment.

  • Lite: Free 30,000 NLU items* per month + 1 free custom model
  • Standard Tier 1: $0.003 per NLU item per month for up to 250,000 items
  • Standard Tier 2: $0.001 per NLU item per month for up to 5,000,000 items
  • Standard Tier 3: $0.0002 per NLU item per month for items past 5,000,000
  • Custom Entities and Relation Models: $800 per model, per month for all tiers
  • Custom Classification Model: $25 per model, per month for all tiers

See the full pricing details here.

Features

  • Text analytics
  • Domain customization
  • Deployment on any cloud platform
  • Entities
  • Categories (Beta)
  • Classifications, concepts, and emotions
  • Sentiment (Beta)
  • Relations
  • Metadata
  • Semantic roles

Microsoft icon.

Azure AI Language

Best for Multilingual Support

Overall Rating: 3.9/5

  • Core Features: 4.6/5
  • Advanced Features: 3/5
  • Cost: 3.7/5
  • Ease of Use: 3.9/5
  • Customization & Integrations: 4.4/5
  • Customer Support: 3.5/5

Microsoft Azure AI Language (formerly Azure Cognitive Service for Language) is a cloud-based service that provides natural language processing (NLP) features and is designed to help businesses harness the power of textual data. It offers a wide range of capabilities, including sentiment analysis, key phrase extraction, entity recognition, and topic moderation. Azure AI Language translates more than 100 languages and dialects, including some deemed at-risk and endangered.

Azure AI interface.
A screencap of creating a new language on the Azure AI portal.

Product Design

Azure AI Language offers a balance between ease of use and flexibility. It has a visual interface that helps users annotate, train, and deploy language models with minimal machine learning expertise. Its dashboard consists of a search bar, which allows users to browse resources, services, and documents. Additionally, a sidebar lets you create new language resources and navigate through its home page, services, SQL database, and more.

Product Development

Azure AI language’s state-of-the-art natural language processing capabilities including Z-Code++ and Azure OpenAI Service is powered by breakthrough AI research. This platform features multilingual models that can be trained in one language and used for multiple other languages. Recently, it has added more features and capabilities for custom sentiment analysis, enhanced text Analytics for the health industry, named entity recognition (NER), personal identifiable information (PII) detection,and more.

Why We Picked Azure AI Language

We chose Azure AI Language because it stands out when it comes to multilingual text analysis. It supports extensive language coverage and is constantly expanding its global reach. Additionally, its pre-built models are specifically designed for multilingual tasks, providing highly accurate analysis.

Pros and Cons

Pros Cons
Pre-built sentiment analysis capabilities No sarcasm detection feature
Supports more than 100 languages and dialects Dependence on single provider

Pricing

  • Free 5,000 text records per month
  • Standard (S) Web: Free to $25 per 1,000 text records

See the full pricing table here.

Features

  • Sentiment analysis and opinion mining
  • Language detection
  • Summarization
  • Key phrase extraction
  • Custom text classification
  • Conversational language understanding
  • Entity linking

Talkwalker icon.

Talkwalker

Best for Social Media Monitoring

Overall Rating: 3.9

  • Core Features: 4.2/5
  • Advanced Features: 4.3/5
  • Cost: 1.5/5
  • Ease of Use: 3.8/5
  • Customization & Integrations: 4.8/5
  • Customer Support: 4.8/5

Talkwalker is a leading social listening platform that provides businesses with actionable social media insights via real-time listening and advanced analytics. This platform goes beyond monitoring social media mentions to offer a robust set of tools for understanding brand sentiment, identifying trends, and engaging with target audiences. Its AI-powered sentiment analysis tool helps users find negative comments or detect basic forms of sarcasm, so they can react to relevant posts immediately.

Talkwalker interface.
A screencap of Talkwalker’s social media listening tool shows sentiment analysis results.

Product Design

Talkwalker has a simple and clean dashboard that helps users monitor social media conversations about a new product, marketing campaign, brand reputation, and more. It offers a quick brand overview that includes KPIs for engagement, volume, sentiment, demographics, and geography. Users can also access graphs for real-time trends and compare multiple brands to easily benchmark against competitors.

Product Development

Talkwalker has recently introduced a new range of features for more accessible and actionable social data. Its current enhancements include using its in-house large language models (LLMs) and generative AI capabilities. With its integration with Blue Silk™ GPT, Talkwalker will leverage AI to provide quick summaries of brand activities, consumer pain points, potential crises, and more.

Why We Picked Talkwalker

Talkwalker helps users access actionable social data with its comprehensive yet easy-to-use social monitoring tools. For instance, users can define their data segmentation in plain language, which gives a better experience even for beginners. Talkwalker also goes beyond text analysis on social media platforms but also dives into lesser-known forums, new mentions, and even image recognition to give users a complete picture of their online brand perception.

Pros and Cons

Pros Cons
Supports 187 languages and 30 social networks Some platform features requires a learning curve
Incorporates visual recognition in monitoring brands Pricing plans aren’t transparent

Pricing

  • Tiers include Listen, Analyze, Business, and Premium

Contact sales to request quotes.

Features

  • Sentiment analysis
  • Social reporting
  • Competitor and follower analysis
  • Post performance
  • Paid campaign tracking
  • Social measurement
  • Influencer identification
  • Customizable reports and dashboards
  • Generative AI capabilities

SAP icon.

SAP HANA Sentiment Analysis

Best for Business Data Analysis

Overall Rating: 3.8/5

  • Core Features: 4.4/5
  • Advanced Features: 4.1/5
  • Cost: 3.7/5
  • Ease of Use: 2.8/5
  • Customization & Integrations: 4/5
  • Customer Support: 3.5/5

SAP HANA Sentiment Analysis is an embedded tool within the SAP HANA platform that lets businesses analyze large and complex streams of data including reviews, social media posts, and surveys to extract insights into their customers’ overall sentiment toward their products or services. With its sentiment analysis tool, users can transform unstructured data into easily understandable categories and generate actionable insights for their business.

SAP HANA interface.
A screencap of SAP Hana’s dashboard displaying customer sentiment in real-time.

Product Design

SAP HANA Sentiment Analysis lets you connect to a data source to extract opinions about products and services. You can prepare and process data for sentiment analysis with its predict room feature and drag-and-drop tool. Its interface also features a properties panel, which lets you select a target variable, and advanced panels to select languages, media types, the option to report profanities, and more.

Product Development

SAP HANA has recently introduced streamlining access administration for its alerts and metrics API feature. Through this development, users can retrieve administration information, which includes alerts for prolonged statements or metrics for tracking memory utilization. Additionally, SAP HANA has upgraded its capabilities for storing, processing, and analyzing data through built-in tools like graphs, spatial functions, documents, machine learning, and predictive analytics features.

Why We Picked SAP HANA Sentiment Analysis

SAP HANA Sentiment Analysis is ideal for analyzing business data and handling large volumes of customer feedback, support tickets, and internal communications with other SAP systems. This platform also provides real-time decision-making, which allows businesses to back up their decision processes and strategies with robust data and incorporate them into specific actions within the SAP ecosystem.

Pros and Cons

Pros Cons
Supported by SAP ecosystem Not as robust as a standalone sentiment analysis tool
In-memory processing allows speed and scalability Interface can be complicated and require more technical expertise

Pricing

SAP HANA’s pricing requires a minimum of 100 capacity units (CU), a three-month contract, and a SAP Business Application Studio license. A capacity unit represents a fixed amount of memory and computing resources.

  • SAP HANA Cloud: Starts at $9.84 per capacity unit
  • SAP Business Application Studio: Starts at $372 per year
  • Free tier option
  • 30-day free trial available

See full pricing details here.

Features

  • Text mining and analysis
  • Data preparation
  • Multi-model engine
  • Business data integration
  • Automated decision-making
  • Context-aware outputs
  • Built-in security features

MonkeyLearn icon.

MonkeyLearn

Best for Workflow Integrations

Overall Rating: 3.6/5

  • Core Features: 4.7/5
  • Advanced Features: 3.8/5
  • Cost: 1.5/5
  • Ease of Use: 3/5
  • Customization & Integrations: 5/5
  • Customer Support: 2.6/5

MonkeyLearn is a cloud-based text mining platform that helps businesses analyze text and visualize data using machine learning. It offers seamless integrations with applications like Zapier, Zendesk, Salesforce, Google Sheets, and other business tools to automate workflows and analyze data at any scale. Through these robust integrations, users can sync help desk platforms, social media, and internal communication apps to ensure that sentiment data is always up-to-date.

MonkeyLearn interface.
A dashboard in MonkeyLearn displays feedback analysis results with sentiments categorized by different aspects.

Product Design

MonkeyLearn features ready-made machine learning models that users can build and train without coding. You can also choose from pre-trained classifiers for a quick start, or easily build sentiment analysis and entity extractors. Its dashboard has a clean interface, with a sidebar displaying filters for selecting the samples used for sentiment analysis. Next to the sidebar is a section for visualization where you can use colorful charts and reports for monitoring sentiments by topic or duration and summarize them in a keyword cloud.

Product Development

MonkeyLearn has recently launched an upgraded version that lets you build text analysis models powered by machine learning. It has redesigned its graphic user interface (GUI) and API with a simpler platform to serve both technical and non-technical users. Additionally, it has included custom extractors and classifiers, so you can train an ML model to extract custom data within text and classify texts into tags.

Why We Picked MonkeyLearn

We chose MonkeyLearn as one of the top sentiment analysis tools because it helps businesses access real-time analysis with easy integrations from third-party apps. This platform also enables users to trigger actions and set up rules based on sentiments, such as escalating negative cases, prioritizing positive comments, or tagging tickets. MonkeyLearn’s workflow integrations provide a holistic view of customer sentiments gathered from various sources, resulting in rich insights and more actionable data.

Pros and Cons

Pros Cons
Robust customization capabilities Rigid pricing model
Easy text analysis integrations in business workflows Limited support for building NLP models in non-English languages

Pricing

  • Free academic plan for educational use
  • Starts at $299 per month

Features

  • Sentiment and text analysis
  • Workflow automation
  • Direct integrations
  • Classifiers and extractors
  • Topic clustering
  • Summarization
  • Language detection
  • Graphical data presentation

Meltwater icon.

Meltwater

Best for Market Research

Overall Rating: 3.6/5

  • Core Features: 4/5
  • Advanced Features: 4.2/5
  • Cost: 1.5/5
  • Ease of Use: 3.8/5
  • Customization & Integrations: 4/5
  • Customer Support: 3.6/5

Meltwater is a media monitoring software and analytics platform that helps businesses handle data across online news, social media, print, broadcast, and podcasts. It deployed its first machine learning models for sentiment analysis and entity recognition way back in 2005 before social media became mainstream. Meltwater supports extensive language detection for 242 languages and provides full sentiment analysis for 218 languages and dialects.

Meltwater interface.
Meltwater’s insight dashboard features sentiment analysis data, social sentiment, total mentions, and more.

Meltwater features intuitive dashboards, customizable searches, and visualizations. Because the platform focuses on big data, it is designed to handle large volumes of data for market research, competitor analysis, and sentiment tracking. Its dashboard displays real-time insights including Google analytics, share of voice (SOV), total mentions, sentiment, and social sentiment, as well as content streams. Monitoring tools are displayed on a single screen, so users don’t need to open multiple tabs to get a 360-degree view of their brand’s health.

Product Development

Meltwater’s latest sentiment analysis model incorporates features such as attention mechanisms, sentence-based embeddings, sentiment override, and more robust reporting tools. With these upgraded features, you can access the highest accuracy scores in the field of natural language processing.

Why We Picked Meltwater

We chose Meltwater as ideal for market research because of its broad coverage, monitoring of social media, news, and a wide range of online sources internationally. This coverage helps businesses understand overall market conversations and compare how their brand is doing alongside their competitors. Meltwater also provides in-depth analysis of various media, such as showing the overall tonality of any given article or mention, which gives you a holistic context of your brand or topic of interest.

Pros and Cons

Pros Cons
Handles 20 billion-plus engagement actions in real-time Sentiment accuracy might not be as robust as other specialized tools
Supports language detection for 242 languages Pricing plans can be expensive for smaller businesses

Pricing

  • Plans include Essentials, Advanced, Suite, and Enterprise

Contact sales to request a quote.

Features

  • Sentiment analysis tool
  • Media database
  • PR insight reports
  • Earned media tracker
  • AI insight explainer
  • UGC management
  • Unified enterprise dashboards
  • Salesforce integration

Idiomatic icon.

Idiomatic

Best for Customer Experience (CX) Analysis

Overall Rating: 3.5/5

  • Core Features: 4.2/5
  • Advanced Features: 4.2/5
  • Cost: 2.2/5
  • Ease of Use: 2.3/5
  • Customization & Integrations: 4.2/5
  • Customer Support: 3.1/5

Idiomatic is an AI-driven customer intelligence platform that helps businesses discover the voice of their customers. It allows you to categorize and quantify customer feedback from a wide range of data sources including reviews, surveys, and support tickets. Its advanced machine learning models let product teams identify customer pain points, drivers, and sentiments across different contact sources.

Idiomatic interface.
An example of sentiment scoring in Idiomatic.

Product Design

Idiomatic’s interface helps users view customer feedback in real time. For example, its dashboard displays data on a volume basis and the categorization of customer feedback on one screen. You can click on each category to see a breakdown of each issue that Idiomatic has detected for each customer, including billing, charge disputes, loan payments, and transferring credit. You can also export the data displayed in the dashboard by clicking the export button on the upper part of the dashboard.

Product Development

Idiomatic has recently introduced its granularity generator feature, which reads tickets, summarizes key themes, and finds sub-granular issues to get a more holistic context of customer feedback. It also developed an evaluating chatbot performance feature, which offers a data-driven approach to a chatbot’s effectiveness so you can discover which workflows or questions bring in more conversions. Additionally, Idiomatic has added a sentiment score tool that calculates the score per ticket and shows the average score per issue, desk channel, and customer segment.

Why We Picked Idiomatic

Idiomatic is an ideal choice for users who need to improve their customer experience, as it goes beyond the positive and negative scores for customer feedback and digs deeper into the root cause. It also helps businesses prioritize issues that can have the greatest impact on customer satisfaction, allowing them to use their resources efficiently.

Pros and Cons

Pros Cons
Deep insights and accuracy in analyzing customer feedback Some configurations have a steep learning curve
Provides actionable guidelines for product teams Quality of results relies on the volume of customer feedback data

Pricing

  • Starts at $399 per month per data source

See full pricing details here.

Features

  • AI-driven customer intelligence
  • Custom sentiment analysis models
  • Custom data labels
  • Ticket group summaries
  • User segments tracker
  • Support tickets routing
  • Report builder
  • Cost per ticket

4 Key Features of Sentiment Analysis Tools

Sentiment analysis tools determine the positive-negative polarity of user-generated text at their most basic level, and offer more advanced tools for working with larger datasets. The best sentiment analysis tools ensure accuracy in analyzing textual data and identify subtle emotions, sarcasm, and how a sentiment relates to the data. There are four key features to consider when selecting a sentiment analysis tool for your business.

Sentiment Analysis and Scoring

Sentiment analysis refers to the process of using computation methods to identify and classify subjective emotions within a text. These emotions (neutral, positive, negative, and more) are quantified through sentiment scoring using natural language processing (NLP) techniques, and these scores are used for comparative studies and trend analysis.

Artificial Intelligence

Sentiment analysis is a subset of AI, employing NLP and machine learning to automatically categorize a text and build models to understand the nuances of sentiment expressions. With AI, users can comprehend how customers perceive a certain product or service by converting human language into a form that machines can interpret.

Data Mining

Data mining is the process of using advanced algorithms to identify patterns and anomalies within large data sets. In sentiment analysis, data mining is used to uncover trends in customer feedback and analyze large volumes of unstructured textual data from surveys, reviews, social media posts, and more.

Multilingual Support

This feature refers to a sentiment analysis tool’s capability to analyze text in multiple languages. Multilingual support is essential in preventing biases, as it promotes an inclusive understanding of languages and cultures and ensures sentiment from global customers is recognized. Understanding multiple languages also helps in training models to understand the complexities of words, phrases, and slang, as one positive or negative sentiment might mean neutral in another language.

How We Evaluated Sentiment Analysis Tools

In assessing the top sentiment analysis tools, we started by identifying the six key criteria for teams and businesses needing a robust sentiment analysis solution. We determined weighted subcriteria for each category and assigned scores from zero to five. Finally, we totaled the scores to determine the winners for each criterion and their respective use cases.

Evaluation Criteria

We placed the most weight on core features and advanced features, as sentiment analysis tools should offer robust capabilities to ensure the accuracy and granularity of data. We then assessed each tool’s cost and ease of use, followed by customization, integrations, and customer support.

Core Features | 30 percent

We evaluated the key offerings of each sentiment analysis tool including sentiment trend, NLP, data mining, machine learning, AI, and more. Criteria Winner: IBM Watson NLU, MonkeyLearn

Advanced Features | 20 percent

Here, we assessed sophisticated features for sentiment analysis such as emotion recognition, multilingual support, intent analysis, and sarcasm detection. Criteria Winner: IBM Watson NLU

Cost | 15 percent

This considers the pricing plans of each sentiment tool, including the starting cost, free trial (if available), billing options, and pricing transparency. Criteria Winner: IBM Watson NLU

Ease of Use | 15 percent

We looked into how real users perceive the usability and the learning curve of each tool across certified review sites. We also evaluated each option’s knowledge base and/or documentation. Criteria Winner: Azure AI Language

Customization and Integrations | 10 percent

This category evaluates the flexibility of each sentiment analysis tool for reports, queries, and text sources. We also considered how seamlessly it integrates with third-party applications and platforms. Criteria Winner: MonkeyLearn

Customer Support | 10 percent

We took into account each tool’s support solutions, such as live chat, phone, and email. We also looked into user feedback and ratings across reputable sites, as well as their respective active user communities. Criteria Winner: Talkwalker

Frequently Asked Questions (FAQs)

What Methods Are Used for Sentiment Analysis?

Although the field of sentiment analysis is relatively new, its methods can be roughly grouped into two categories:

  • Statistical Techniques: Statistical methods deploy tool sets from AI, deep learning and machine learning. These technologies dig into grammatical relationships, sometimes attempting to differentiate the subject of the opinion from the users offering those opinions.
  • Knowledge Techniques: This methodology identifies “sign post” words that tend to be clear identifies of emotion, ranging from “love” to “hate” to “adore” to “lame.” At its more advanced, knowledge techniques can evaluate words on a spectrum of like/dislike.

Many sentiment analysis tools use a combined hybrid approach of these two techniques to mix tools and create a more nuanced sentiment analysis portrait of the given subject.

How Should You Choose a Sentiment Analysis Tool?

Sentiment analysis tools are variously described as performing opinion extraction, subjectivity analysis, opinion mining or sentiment mining. In either case, choosing the best sentiment analysis tool for your company typically includes considering the following:

  • Volume of Material: Estimate the amount you want to analyze. If your company is truly hoping for a deep, across-the-market sentiment analysis, choose a more robust tool.
  • Test the Software: Does the software give an accurate view of the market’s opinion? If you look at the text yourself, does the software agree with human understanding?
  • Total Features: Sentiment analysis is a complex process, and your sentiment analysis tool should work well with unstructured data. It’s essential to know the F1 score, which is a measure of a statistical test’s accuracy.
  • Pricing vs. Assumed Value: While this might be difficult to fully compute, you can look at the price tag on the sentiment analysis software and then ask yourself if you’re going to make many more times that amount (in profit) based on the insight from this tool.

What Questions Do Users Ask of Sentiment Analysis Tools?

Here are some examples of the questions that users ask of sentiment analysis software:

  • Products: How are consumers responding to the new microwave?
  • Movies: How is word of mouth forming the overall view of this film?
  • Politics: Is this candidate resonating with the broader electorate?
  • Technology and SaaS: What features do users request more frequently?
  • Healthcare: What are the top reasons for negative sentiments in patient surveys?

Bottom Line: The Best Sentiment Analysis Tool for 2024

Sentiment analysis tools enable businesses to understand the most relevant and impactful feedback from their target audience, providing more actionable insights for decision-making. The best sentiment analysis tools go beyond the basics of positivity and negativity and allow users to recognize subtle emotions, more holistic contexts, and sentiment across diverse channels.

Read our in-depth guide to the top sentiment analysis solutions, consider feedback from active users and industry experts, and test the software through free trials or demos to find the best tool for your business.

If you’d like to know more about data mining, one of the essential features of sentiment analysis, read our in-depth guide on the types and examples of data mining.

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