Some of the best insights you get about your business are from the opinions and feelings your customers have about their customer journey with you. It is vital that brands extract and score this customer data to get these insights. In turn, you will be able to improve your business processes with data-backed business intelligence, develop effective strategies for growth, and build your brand’s reputation. To do this, you need the right Sentiment Analysis API that has exceptional named entity recognition (NER), text analysis, video AI, and other ML capabilities.
With that in mind, let us explore the top sentiment analysis tools and the most important factors you should consider when selecting a Sentiment Analysis API for your business.
What Are the Most Important Factors in Selecting the Best Sentiment Analysis Tool?
With the right sentiment analysis tool, and through emotion analysis, you will be able to analyze vast amounts of positive, neutral, and negative data. This, in turn, empowers you to understand your customers and employees better, no matter what your industry.
To enable you to do this effectively, the right sentiment analysis tool should have key features like multilingual efficacy, precise aspect-based sentiment analysis, named entity recognition, an effective visualization dashboard, and more.
Let us look at the features you should consider in more detail.
1. Speed and scale
One of the most crucial sentiment analysis features is speed. It allows you to process text and comments, and gain insights from it faster.
2. Accuracy
A sentiment analysis tool should be able to precisely score sentiments with a great deal of accuracy.
3. Aspect-based analysis
Aspect-based sentiment analysis is one of the fastest and most granular ways to get insights and a more detailed understanding of your customers’ opinions which, in turn, improves your emotion analysis efforts.
4. Multilingual
When you serve multiple markets or you want to grow quickly into other countries, the multilingual capabilities of a sentiment analysis tool will help you accurately analyze data in many different languages.
5. Multimedia
It’s crucial that the Sentiment Analysis API not be limited to only text and should be able to perform sentiment analysis on both video and audio data as well.
Which Are the Top 8 Tools for Analyzing Sentiment?
There are many Sentiment Analysis platforms, which makes it challenging to find the right one. With that in mind, we did some testing to compare the top sentiment analysis tools. It’s good to bear in mind that all of these tools have their own pros, cons, and features you’ll need to consider, taking into account the key features we mentioned earlier. When doing so, and by comparing these sentiment analysis tools, you will find the right one for your specific needs and requirements. That said, let us look at the top Sentiment Analysis APIs in more detail.
1. Repustate
Repustate offers entity extraction and sentiment analysis capabilities that allow you to extract insights from unstructured data and use emotion analysis to understand the behavioural patterns and motivations of your customers. More importantly, it enables you to access video-based data from sources as different as academic videos, instructional videos, user-generated content on social media like YouTube or TikTok, and even entertainment videos. Being especially adaptable to social media listening data, Repustate recognizes hashtags, emojis, differentiates between Twitter handles and Twitter entities, social lingo, abbreviations, and pseudonyms for popular brands.
Its advanced sentiment analysis API is the fastest sentiment analytics engine on the market that works in tandem with text and video content including text overlays in them. The Repustate engine uses semantic search, natural language processing (NLP), and machine learning models that allow you to create custom sentiment rules tailored specifically for your industry and needs. It also allows you to use aspect-based sentiment analysis that isolates the sentiment for each topic and offers more detailed, actionable insights.
From a business perspective, it is great for enterprises but also for small and medium businesses who need a limited number of seats unlike larger enterprises who need the application at scale. Here are some sentiment analysis use cases that showcase how we have helped real businesses.
2. Google Cloud NLP
Google Cloud NLP allows you to use entity extraction and sentiment analysis to extract and analyze text from various sources like emails, chats, and social media, so you can gain valuable insights and a better understanding of your customers. It also offers video content analysis but does not feature the capability to analyze text overlays.
The tool can, however, be confusing at times. As such, sometimes it works really well and other times it performs confusingly poorly. For example, it was one of the few Sentiment Analysis APIs to factor in aliases and correct common spelling mistakes. In contrast, in the sentence, “Brian Cox starts in the HBO show ‘Succession’,” it didn’t recognize the show name and selected the wrong Brian Cox.
3. Microsoft Azure Cognitive
Microsoft Azure Cognitive Services, through text analytics, allows you to identify key phrases and entities and gain valuable insights from unstructured text in a wide range of languages. In this way, you get a deeper understanding of your customers, common topics, and trends. Like Google Cloud NLP, it also offers video content analysis without the ability to analyze text overlays.
During testing, it was the only Sentiment Analysis API that could qualify numerical values like temperatures, percentages, and dates at a very granular level. However, it is severely hampered by its inability to properly classify people which omits a lot of context and limits how much analysis can be done. Interestingly, it was also the slowest of all the platforms in our testing.
4. Dandelion
Dandelion uses entity extraction and sentiment analysis to identify and find mentions of people, places, and businesses and identifies whether opinions in short texts are positive, negative, or neutral. It also has multilingual capabilities and the ability to identify relevant and important key phrases in social media posts and articles.
Although its features sound similar to many other Sentiment Analysis APIs, it does have some drawbacks. Like most of the other APIs here, it could not associate noun “qualifiers” later in a sentence to nouns appearing earlier. Also, like many of the other APIs, it could not resolve Twitter usernames to their underlying entity. Also, it only has support for 7 languages and is reasonably slow.
5. Aylien
Specifically aimed at extracting and analyzing news, Aylien allows you to aggregate and analyze news feeds from over 80,000 sources and gain valuable insights from them. As such, it features entity extraction and sentiment analysis. In practice, the solution leaves a lot of room for want, though.
Like some of the other Sentiment Analysis APIs, it struggled with context disambiguation accuracy with more complex sentences and lacked a sufficient level of granularity. Another thing to note was that during our testing, Aylien was the least accurate. The platform supports only six languages.
Also Read: Guide to Sentiment Analysis
