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Top 7 Sentiment Analysis Tools in 2024: An In-depth Guide

Sentiment analysis is a Natural Language Processing (NLP) technique that categorizes texts, audio, images, or videos as positive, negative, or neutral based on their emotional tone. It helps businesses understand customer sentiment and improve their services and products based on customer needs. 

In 2023, it is estimated that more than 80% of companies will switch to a technology that analyzes the sentiment in customers’ reviews or feedback. We prepared this guide to inform businesses about their sentiment analysis tools and vendor selection, and to help them find the best service that suits their needs.

Top 7 sentiment analysis tools

The data is gathered from the websites of vendors. You can sort Table 1, for example, by multilingual sentiment analysis capabilities (i.e., applicable or not applicable) or by other columns:

Vendor NameFree trialData pre-processingAudio analysisImage analysisVideo analysisMultilingual analysisSentiment granularityDomain-specific models
Microsoft Azure Cognitive Services✖️✖️✖️Sentiment probability score
MonkeyLearn✖️✖️✖️✖️✖️Sentiment by categories, over time, and intentNot provided
Repustate✖️Not providedSpecialized in customer, employee, and patient experience
TalkwalkerCustomized free demo is offeredNot provided✖️✖️Sentiment by featuresNot provided
Meaningcloud✖️✖️✖️✖️Global sentiment polaritySpecialized in banking, insurance, and healthcare
DandelionNot provided✖️✖️✖️Not providedNot provided
TextRazor✖️✖️✖️✖️Not provided

Vendor selection criteria

We narrowed our vendor list based on some criteria. We used the number of B2B reviews and employees of a company to estimate its market presence because these criteria are public and verifiable.

Therefore, we set certain limits to focus our work on top companies in terms of market presence, selecting firms with

  • 10+ employees
  • 10+ reviews on review platforms including G2, Trustradius, Capterra

As all vendors offer customizable API connections and real-time analysis, we did not include these capabilities in the table. Below you can see our analysis of sentiment analysis tools in terms of the capabilities and features of sentiment analysis tools.

Sponsored

Clickworker provides sentiment analysis services with 6+ million workers in which they analyze and process large data sets by leveraging human intelligence to determine customer sentiment accurately. They can help by quickly and efficiently tagging your large database of texts, images, or videos with relevant terms.

Here is a sample task for Clickworkers to categorize sentiment in a text.

Figure 1. Sample task for the annotators to categorize the sentiment in a text.

9 sentiment analysis tools capabilities

Not all companies have the same needs or customer types. That’s why determining selection criteria based on customer profile or the company needs is crucial in the vendor selection process. Here are 9 essential features and capabilities of sentiment analysis tools that businesses should consider while selecting a sentiment analysis service provider:

1- Customizable API connection

The exchange of data between companies can be challenging as they may use different systems or applications to keep their data. A customizable API connection allows businesses to send data to each other safely and quickly. Besides, companies having various social listening channels to collect feedback (e.g., Instagram, Twitter, TikTok) can easily merge them for comprehensive social media sentiment analysis through a customized API connection.

For more on social media sentiment analysis, check out our article.

2- Real-time analysis

Applications like customer service or social media monitoring may require real-time sentiment analysis. Companies may quickly learn how customers feel about their brand by using social media platforms like Twitter or TikTok to collect data. This can enable quick response in the event of a negative experience.

For those interested, here is our article on Twitter sentiment analysis.

3- Data preprocessing

The accuracy of sentiment analysis results can be increased by using tools that contain data preprocessing features like text cleaning, normalization, and stemming. If your text data is messy or unstructured, look for technologies that provide these qualities.

4- Audio analysis

Although most sentiment analysis tools analyze customers’ sentiment in their reviews or written feedback, research shows that 76% of customers prefer contacting companies by phone. Thus, tools analyzing customers’ voices can help companies with high call center traffic. 

For those interested, here is our article on audio sentiment analysis

5- Image analysis

Image sentiment analysis enables the classification of the overall sentiment conveyed by the image, usually through facial expressions and body configurations. Besides, image analysis requires understanding the indicators of positive and negative experiences. 

For instance, in the hospitality industry, while the image of an outside view of buildings in the reviews can be classified as positive, floor patterns can be categorized as negative (see Figure 2).

Image sentiment analysis on hotel reviews.

Source: Research Collection School Of Information Systems

Figure 2. Comparison of positive and negative sentiment in hotel reviews.

6- Video analysis

Online videos reached about 93% of Internet users in 2022, becoming one of the most used online formats.1

Companies can get help from video sentiment analysis in understanding the feelings or thoughts about a certain topic, product, or brand. This can help get informed about ideas that otherwise would go unnoticed in text sentiment analysis.

7- Multilingual analysis

Global companies serve customers from all over the world, which increases the variety of languages used in the reviews or feedback.  

Multilingual analysis tools can help businesses learn customers’ feelings and thoughts using different languages and perhaps living in other areas. This helps them provide more customized solutions to their customers’ needs. 

Learn about the challenges and methods of multilingual sentiment analysis.

8- Sentiment granularity

While some sentiment analysis tools categorize text data as positive, negative, or neutral, others offer more detailed sentiment scores, such as a 1–10 scale or a score for particular emotions. While selecting the best sentiment analysis tool, think about the granularity level that is most crucial for your use case.

9- Domain-specific models

Using sentiment analysis tools that offer domain-specific models to analyze text data in a specific domain or industry can provide more reliable results. A sentiment analysis tool specializing in analyzing hotel reviews, for example, would have a different model than one specializing in analyzing financial news.

For more in-depth knowledge on sentiment analysis, download our whitepaper:

Get Sentiment Analysis Whitepaper

You can also check out our data-driven list of sentiment analysis services.

If you have any questions about sentiment analysis services, don’t hesitate to reach us:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Begüm Yılmaz
Begüm is an Industry Analyst at AIMultiple. She holds a bachelor's degree from Bogazici University and specializes in sentiment analysis, survey research, content writing services and Customer Relationship Management (CRM) systems.

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