Top 3 Sentiment Analysis Services in 2025

While many sentiment analysis tools are available, an emerging trend is the use of sentiment analysis services, which offer the advantages of crowdsourced human-labeled data and domain-specific expertise.

Learn the essential features of sentiment analysis services and see a benchmarking study that compares the characteristics of various service providers to help businesses choose the best option for their needs:

Comparison of sentiment analysis services

Table 1. Market presence comparison

* Reviews are derived from B2B software review platforms

** Based on LinkedIn

Table 2. Feature comparison

All vendors enable users to achieve quality assurance through majority decision.

Disclaimer: N/A indicates that the information has not been publicly disclosed.

Clickworker

Clickworker offers AI-powered sentiment analysis to help businesses better understand customer emotions and interactions. Using natural language processing (NLP) and machine learning, we analyze textual data to categorize sentiment as positive, negative, or neutral. This provides valuable insights from various sources like social media posts, customer feedback, and surveys.

Their sentiment analysis tool help businesses:

• Gain a deeper understanding of customer sentiment across multiple data sources.
• Improve marketing and public relations strategies with valuable insights into customer emotions.
• Refine AI models using human-verified data, resulting in more precise outcomes.

Clickworker’s services allow businesses to process large volumes of data efficiently, making it easier to train and improve AI systems for better customer experience management. Clickworker’s approach is built on rigorous quality assurance, data security, and sustainability practices.

Appen

Appen’s sentiment analysis services leverage natural language processing (NLP) and machine learning to help businesses analyze customer sentiment across various social media platforms and customer interactions. By utilizing advanced sentiment analysis tools and algorithms, Appen enables the detection of positive, negative, or neutral sentiments from unstructured data like social media posts and survey responses.

Their sentiment analysis models provide actionable insights that improve customer satisfaction and brand reputation, supporting marketing campaigns and customer service teams. With a multilingual sentiment analysis system and a focus on text data, Appen helps businesses track sentiment, analyze customer feedback, and enhance their overall sentiment scores to better understand public opinion and customer emotions.

Brandwatch

Brandwatch’s sentiment analysis service utilizes advanced sentiment analysis tools, leveraging natural language processing (NLP) to effectively analyze sentiment in customer feedback across various social media platforms. Their PhD-qualified team employs a hybrid approach, combining knowledge-based, machine learning, and rules-based techniques to accurately detect positive, negative, and neutral sentiments in unstructured data.

This sentiment analysis model allows for the analysis of textual data with a focus on actionable insights, enhancing customer satisfaction and brand reputation. By incorporating feedback from social media monitoring, Brandwatch aims to provide meaningful insights that help brands understand customer opinions and identify gaps in their marketing campaigns.

Vendor selection criteria:

To narrow down our study, we have determined some metrics and included companies that fit these criteria. We have included vendors that have:

• 15+ reviews on B2B review platforms
• 1000+ employees

As all vendors offer customized service and multilingual analysis, we did not include these metrics in our research. You can find our benchmarking study in the table below. Note that the data is based on vendor claims.

AI in sentiment analysis

Sentiment analysis uses artificial intelligence (AI) to determine how people feel about a particular topic by analyzing text data. It helps businesses understand customer emotions in reviews, feedback, and social media posts. AI-powered sentiment analysis follows a structured approach:(Taherdoost, H., & Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review in competitive research. Computers, 12(2), 37.)

Preprocessing data:

• Before AI can analyze text, it needs to be cleaned. This step is called preprocessing.
• Text is processed to handle punctuation, emojis, and spaces, making it understandable for AI systems.
• Proper preprocessing improves the accuracy of sentiment analysis.

Natural language processing (NLP):

• AI uses NLP to understand the meaning of words and phrases in text.
• It identifies specific emotions in the text, like whether a review is positive, negative, or neutral.
• Advanced AI can even detect tone and deeper emotions, sometimes better than humans.

Machine learning and deep learning:

• Machine learning helps AI recognize patterns in data.
• AI is trained on labeled data, where each piece of text is already tagged with the correct sentiment.
• Deep learning allows the AI to process large volumes of data and improve its predictions over time.

Benefits of AI in sentiment analysis

• Efficiency: Processes large amounts of text quickly.
• Effectiveness: Detects emotions and opinions with high accuracy.
• Improvement: Learns and improves over time, making predictions more reliable.

Challenges of AI in sentiment analysis

• Context understanding: AI can struggle to understand the context of language, leading to misinterpretations. This is especially tricky with sarcasm, irony, or cultural differences.
• Data privacy: Analyzing customer data for sentiment raises concerns about privacy and security.
• Bias and fairness: AI models can unintentionally include biases from training data, resulting in unfair outcomes. It’s important to address these biases and ensure fairness in sentiment analysis.

What are sentiment analysis services?

A sentiment analysis software is a type of Natural Language Processing (NLP) method that provides crowdsourced human-labeled data to assess the sentiment or emotional tone portrayed in a text, audio, image, or video. Services that perform sentiment analysis classify content as positive, negative, or neutral using human annotators rather than relying only on machine learning algorithms.

Sentiment analysis, also known as opinion mining, is a technology that enables businesses to gain insights into the opinions, emotions, and attitudes of their customers and other stakeholders. Machine learning algorithms use large amounts of data to learn patterns and make predictions when analyzing sentiments. 

However, accurately classifying sentiment in a text, audio, image, or video can be difficult due to complex and subtle nuances in human language that may not be captured by automated approaches alone. This is where human-labeled data can come in handy.

Why is human-labeled data crucial in training machine learning algorithms?

Data must be labeled accurately to help machines “understand.” Human annotators can label the data based on the emotional tone of the content, such as detecting sarcasm or irony in them. Manual labeling to train the data is cost-effective, more accurate, and more flexible than automated labeling.

High-quality training data is essential for building and improving AI models. Clickworker, with its network of over 6 million contributors, provides human-labeled datasets for AI training. Their services include generating, labeling, and validating datasets such as text, images, audio, and video. Human involvement ensures that AI models learn from well-prepared data, leading to better results. Clickworker also helps assess AI outputs, ensuring that machine learning models perform accurately and align with real-world expectations.

8 essential factors in choosing the best sentiment analysis service

When evaluating sentiment analysis services, there are a few key features to consider that can help narrow down the options and make a more informed decision:

1- Quality assurance

The development and deployment of sentiment analysis services include quality assurance as a crucial step since it makes sure the service satisfies the needs and expectations of its customers and offers practical insights into their sentiments and behavior. A good service provider will have quality assurance practices in place like using the majority vote of the annotators which can be used to guarantee quality.

2- Customized service

In sentiment analysis services, customized service refers to a service’s ability to be tailored to the specific needs and requirements of a user or application. Customization options may include adding or removing sentiment categories, training the service on a particular domain or language, or adjusting the sensitivity of the sentiment analysis based on the context of the text. While selecting a service, watch out for the level of customization offered.

3- Audio sentiment analysis

The ability of businesses to analyze and interpret sentiment from spoken languages, such as phone calls or recorded meetings, makes audio analysis an essential part of sentiment analysis services. For companies that rely extensively on phone-based customer assistance or telemarketing, audio analysis can offer insightful information about customers’ feelings, thoughts, and attitudes.

For more on audio sentiment analysis, check out our article.

4- Image sentiment analysis

Image sentiment analysis allows businesses to analyze and interpret sentiment from visual content like photos. It can be handy for companies that rely heavily on social media or online advertising. 

Businesses can better understand customer sentiment and behavior and make more educated decisions about product development, marketing, and customer engagement by integrating image analysis into their sentiment analysis services.

5- Video sentiment analysis

Using video analysis, businesses can assess sentiment from video content such as ads, product evaluations, or client testimonials. Companies that produce a lot of video material or rely heavily on video marketing can benefit greatly from video analysis.

6- Multilingual analysis

For enterprises and organizations that operate in international or multilingual environments, sentiment analysis services that support multilingual analysis can offer insights into customer sentiment and behavior across various markets and regions.

Here is our article on multilingual sentiment analysis challenges and methods for those interested.

7- Price

The prices of sentiment analysis services vary greatly depending on the features and capabilities provided, the volume of data analyzed, and the level of customization required. Cost may be a critical factor in the decision to use a sentiment analysis service for small businesses and organizations with limited budgets, and lower-cost options may be preferred over more expensive alternatives.

On the other hand, larger corporations and organizations might place a higher value on accuracy, customization, and scalability than on price, and they might be prepared to pay more for a sentiment analysis solution that caters to their particular requirements.

8- Customer support

Getting efficient and high-quality customer support from the service providers is essential to get the best out of what companies pay for. 

Businesses can search for the availability of customer service representatives (i.e., whether through 24/7 support or e-mail between working hours), as getting updates through the process would be easier and faster. In addition, organizations can benefit from and be guided by customer service professionals’ expertise in a particular field, in this case, sentiment analysis.

Further Readings

• Top Applications of E-Commerce Sentiment Analysis
• MonkeyLearn Evaluation and Top Alternatives
• Sentiment Analysis Benchmark Testing: ChatGPT, Claude & DeepSeek