Top 8 Sentiment Analysis Companies in 2023
As machine learning (ML) gains ground in the business sphere, there are many sentiment analysis companies that provide in-depth text analytics and sentiment mining insights for customer experience analysis, employee experience analytics, and more. Here is a look at some of the top companies that stand out in terms of accuracy of insights, granularity, ease of use, cost-effectiveness, and reliability in various target markets and industries.
Which Are The Top 8 Sentiment Analysis Companies?
Among the many top sentiment analysis companies that offer an efficient text analytics API for emotion detection in data. Here are eight that stand out.
Based in Toronto, Canada, Repustate provides sentiment analysis insights powered by artificial intelligence and machine learning for customer experience, employee experience, and brand experience. The company’s sentiment analysis API extracts entities, topics, themes, and aspects for granular sentiment mining from any source and data type such as text, audio, and video. It has robust named entity recognition (NER) and deep-learning-based semantic classification capabilities that make it one of the most precise tools on the market. It is completely free of code, which means the user can use the tool for detailed insights across parameters, as soon as it is plugged in.
Repustate’s API is also available as a complete platform with a comprehensive dashboard. Both applications seamlessly identify and extract important words and phrases, sentiment trends, author-driven insights, and more to provide crucial intelligence that includes aspect-emotion co-occurrence patterns, insights based on audience/data source, as well as language-driven ones. The solution is available in 23 languages and serves industries such as healthcare, banking, public service, education, market research, and even PR agencies to analyze customer experience for branding strategies.
Google is a prominent name in the sentiment analysis field mostly due to its ubiquity in the ML industry. Its Google NLP API allows you to analyze text documents such as emails, articles, chatbot histories, web pages, etc. The API is great for software developers due to the coding requirements in the tool. It is cost-effective but it’s important to note that it provides very basic, albeit, accurate overall sentiment analysis and entity-level sentiment analysis. For aspect-driven or other deeper insights, one needs to switch to their Vertex API at an additional cost.
Google provides sentiment analysis in 11 languages and entity-level for 3. The product can easily be used for simple tasks if you are technically savvy. One thing to consider is that Google’s API does not provide text extraction, which is crucial for keyword recognition, but the API makes up for it in its simplicity and REST API-based access.
3. Microsoft Azure API
Again, Microsoft is one of those sentiment analysis companies that does not need any introduction, thanks to its popular Microsoft Azure sentiment analysis API that provides text analytics and emotion detection from data easily. The tool can analyze quantifiable metrics like dates, currencies, percentiles, etc, as easily as qualitative data. Its entity classification requires more accuracy though, as the tool cannot classify a wide range of entities, which is critical for high-precision insights.
Azure is simple to understand and easy for beginners. It has good documentation and offers free user guides to help new users understand the tool. The tool can be on the expensive side and there is an issue with downtime whenever there is an Azure outage. But it’s Microsoft, so you have great data security and ofcourse, support.
Dandelion is a sentiment analysis company that is based in Italy. It offers emotion-mining insights and can be used in various industries such as banking, telecommunication, and others. Dandelion’s API can analyze 7 languages and provides reasonably fair insights. The API is good for you if you simply want an aggregate overall sentiment score from your data, as some companies do, who are new to sentiment analysis, for marketing purposes.
It identifies important concepts and key phrases in articles, social media data, blogs, etc. Technically, it cannot match noun qualifiers to nouns that occur later in the data, which lowers its accuracy. Nonetheless, Dandelion offers good text classification, language detection, and sentiment mining. It is especially good for short text data such as Twitter feeds, and can identify hashtags pretty accurately.
Aylien is a text analytics company headquartered in Dublin, Ireland. Its Aylien API is designed for news and online publications and can analyze data in 6 languages. It has a quick processing speed and can be easily integrated into various systems. The API is used by agencies such as Deloitte, Wells Fargo, and others for news analysis. The tool has good documentation and can extract aggregate sentiment scores for news trends from different sources.
Additionally, like some other sentiment analysis companies, Aylien too provides real-time insights into tagged and structured news feeds. It can access around 80,000 news sources, which include paid websites. During search queries, the API produces wide-strewn results, which is good for a bird’s eye view of the topic but that may also lead to irrelevant results.
6. Amazon Comprehend API
Amazon’s sentiment analysis tool from Amazon Web Services (AWS), Comprehend API, is an easy-to-use tool that can identify and analyze data in 6 languages. It can extract key phrases and topics from various data sources and detects emotion easily. It has a fairly easy user interface (UI), which is good for beginners. Like Repustate, Comprehend too can analyze audio transcripts such as those in customer care calls for customer feedback analysis.
It is great for huge volumes of data, which most enterprises have, as it is only then that the Comprehend API can provide higher accuracy in its results. Topic modeling can be a tad complicated for those not familiar with coding as well. The API produces results in zip files that need to be extracted to get the information. It may not be a cost-effective solution for start-ups or for those organizations who are interested in ML technologies for business growth but want to start conservatively.
spaCy is a free, open-source library meant for natural language understanding (NLU) systems and provides sentiment analysis insights for several languages. It’s written in Cython and provides excellent stemming, tokening, part-of-speech tagging, etc. The API can be easily integrated with data science software in the backend and processes natural language queries. Unlike other sentiment analysis companies mentioned in this list, being open-source, spaCy gets you a lot of community support in case of snags and you also get help with state-of-the-art algorithms easily from community members.
The API is apt for the technically savvy as it can get a bit technical and complicated to use by regular users such as those in the marketing department. The same goes for its user interface. However, it is great for generic uses and data management. Documentation is excellent as well and the API also provides several examples so that someone new to the system can practice and get better.
TextRazor is a UK-based text analysis sentiment analysis company that provides emotion detection in 12 languages from various data sources such as legal documents, news articles, surveys, etc. It offers entity classification, key phrase recognition, semantic classification, and such, though it doesn’t provide text cleaning, which can affect overall accuracy. TextRazor caters well to European companies and provides analysis also in Greek and Ukrainian languages.
How To Select The Best Sentiment Analysis Company?
When you choose a solution from among the top sentiment analysis companies, there are still certain features that you need to consider when purchasing your software. The points below explain them briefly.
When you choose an API from the sentiment analysis companies you have short-listed, you need to ensure that the tool has high speed. This is very important in marketing uses such as for customer experience analysis insights for brand monitoring, or for time-critical industries such as fintech.
You may want to consider that even though you may be new to sentiment mining for CX or EX needs, your business will grow and you will eventually need more analytics. This should not mean that you make additional investments again in upgrading your software. That’s why it is important that the sentiment analysis solution you choose is scalable.
High accuracy insights come from robust NER and NLP capabilities. This includes the tool’s ability to recognize misspelled words, entity recognition, keyword recognition, accurate tokenization, and such. You must choose the tool that gives the most accurate results based on your industry and business needs.
Having an aggregate sentiment score doesn’t help much if you’re looking for granular details such as industry insights, market trends, customer engagement drivers, employee experience insights. etc. Only aspect-based sentiment analysis can give you real, actionable, fine-grained insights.
Most sentiment analysis companies offer multilingual text analysis. But the thing to note is that those insights are driven by native speech taggers and not translations because translations can dilute data clarity.
6. Social Media
Social media sentiment analysis is a very crucial part of marketing strategies and your sentiment analysis API or platform should be able to extract meaningful insights from social data. This means that the tool should be able to read hashtags and keywords, recognize important names, understand jargon, and such so you can use it for various purposes such as to find TikTok influencers or analyze Twitter feeds in real-time.
You should be able to analyze text, audio, and video data, which is why your tool should have efficient and accurate multimedia analysis capabilities driven by cutting-edge ML techniques.
8. Entity Extraction
Entity recognition and extraction give you insights that are related to your industry and business. Only then your industry-specific model can give you real insights and service aspects and emotions related to them.
9. Semantic analysis
With efficient semantic analysis you get insights that are precise because it cuts out redundant data. This is because the technology allows the API to recognize contextual meanings behind words. Not many sentiment analysis companies have extremely high semantic capabilities.
10. Reporting Dashboard
Your sentiment analysis software should be available as a dashboard as well because only then can you understand the insights. Unless you already are using a tool like Tableau or PowerBI, which the sentiment analysis API can integrate with, you will be needing a visualization dashboard for the mined results.
Repustate provides the most comprehensive insights from a wide range of data including social listening on Instagram, surveys, news sources, and more. The sentiment analysis solution is available as an API as well as a full-fledged platform. Repustate is one of the few sentiment analysis companies that offer aspect-based sentiment analysis without additional software requirements or costs. It analyzes 23 languages, all natively, and gives you accurate insights upward of 85%.
The solution detects all languages automatically and provides semantic analysis in them seamlessly so you get high-precision insights regardless of language. Use it for review sentiment analysis, or to get employee experience insights flawlessly, whatever your need, without hassle.