How To Identify The Best Sentiment Analysis Platform Suited To You
A sentiment analysis platform provides you with the right insights you need to increase customer satisfaction and engagement, better products and services, and discover emerging market trends. The important thing, though, is to make sure that you are using the best sentiment analysis tool for your business - one that is the best fit for your business and industry.
This article tells you exactly what you should pay attention to when looking for a platform to analyze sentiment in feedback and social media data.
What Is Sentiment Analysis?
Sentiment analysis is a machine learning (ML) enabled, automated process, of extracting sentiment from data, which could be customer experience related or employee-related. Several ML tasks such as natural language processing (NLP), semantic clustering, named entity recognition (NER), neural networks, sentiment analysis, and more, work together in order to derive intelligent insights from said data.
Companies use this emotion mining data in order to better understand customer intent, develop better targeting marketing strategies, offer better services, build a better work culture, and increase customer and employee satisfaction.
Why Do Companies Need A Sentiment Analysis Platform?
Businesses need a sentiment analysis platform so that they can get accurate, high-quality insights from customer or employee experience feedback in a user-friendly manner.
An AI-driven platform goes through many sentiment analysis steps involving numerous machine learning tasks and eventually provides fine-grained insights that manual analysis of data can’t provide.
In this way, it brings companies several invaluable benefits, chief among which, are mentioned below.
1. Data Integrations
An astute model can integrate with and analyze customer experience data from across numerous sources including customer surveys, employee surveys, patient voice data, emails, online sources, chatbots, review websites, video data, and more.
2. Social media listening
You can get effortless social media listening insights from the most popular social networks, review sites, and news sources, and listen to what customers are saying.
3. Brand reputation management
A sentiment analysis platform can give you brand experience insights that you can use to ensure that your brand is protected against negative reviews. You can also find innovative ideas for brand amplification.
4. Discover emerging trends
Detailed customer experience analysis from comments and reviews in user-generated social content can show emerging market trends driven by customer intent. This is extremely useful for planning for product enhancements and service improvements.
5. Comparative analysis of historical and current data
Through a comparison of historical and current data you can see insights across timelines. This can give you indications as to what lead to growth or decline in customer satisfaction so you can dig deeper to find out why.
6. Real-time sentiment analysis
A sentiment analysis platform can offer you real-time sentiment insights from live video streams as well as live comments and reviews. This is very useful during launch events, corporate seminars, live radio broadcasts, and more, to see public sentiment and engagement with your brand.
7. Get alerts and notifications
A business can get real-time notifications in the form of alerts whenever there is a negative rating because of a bad customer experience, brand mention, or a spike in mentions or hashtags. This allows you to provide a timely response, especially on a social channel.
8. Accurate, objective feedback insights
Most importantly, you get an objective analysis of all customer feedback - both negative and positive. Human limitations can lead to a bias in understanding or acknowledging negative feedback. This problem is sorted out through an AI model that is trained to recognize sentiment regardless of the responder.
A sentiment analysis platform can scale infinitely as you grow in business and market share. It can scan and analyze terabytes of data for customer feedback analysis in seconds, regardless of the source. This allows you speed to market and also in developing the right marketing content strategy.
10. The AI model keeps getting smarter
Because AI algorithms work on neutral networks that remember patterns in data, the longer you use the sentiment analysis platform to analyze your customer experience data, the smarter it becomes over time without additional training. It is truly an investment that keeps on giving.
How Do You Identify The Best Sentiment Analysis Platform For Your Business?
To get the best ROI on a sentiment analysis platform for your business you need to make sure that you get the most accurate and granular insights. Below are some of the important features that the platform you choose must have.
1. Omnichannel Insights including video content analysis
Your sentiment analysis platform should be able to extract customer or employee insights from all major online sources including social media, online review channels, podcasts, video platforms, surveys, and more.
2. Brand Sentiment Score
You should be able to get an overall sentiment score of your business that you can see insights based on time intervals - monthly or per annum. This allows you to have a holistic view of your brand experience and see how the market perceives your brand, business, or product.
3. Aspect Word Cloud
Apart from charts and graphs, you should be able to get a user-friendly overview of prominent aspects that the sentiment analysis platform discovers in comments and reviews. This is especially useful in a survey or for qualitative social media metrics for say, Instagram social listening.
4. Market Sentiment Trend Over Time
The model should be able to discover patterns in sentiment over time. This allows you to keep track of how your business is performing in customer satisfaction, and what events have contributed to dips or spikes in customer sentiment.
5. Aspect-Emotion Co-occurrence
Another important feature your tool must have is the ability to show granular sentiment scores for each aspect it discovers from your data. This tells you which emotion or sentiment corresponds to which aspect of your business so you know exactly what to do to improve yourself.
6. Topic Classifications
The sentiment analysis platform needs to be capable of automatically classifying and categorizing keyword and noting their frequencies. This is very important because it can give you insights based on customer demography, competitor brands, regions, and other aspects that influence customer experience.
7. Semantic insights
Semantic classification ensures that you don’t get redundant results, which can make your insights inaccurate. Semantic analysis ensures that similar words are clustered together and their sentiment is scored on an overall basis. It also ensures that incorrect grammar and misspelled words, especially proper nouns of people, places, brands, etc, are clubbed together as well and not ignored. This is vital for comments from social media.
Repustate’s sentiment analysis platform even allows you to see the exact phrases in the reviews that constituted a particular aspect. This gives you an in-depth peek into customer reviews, especially if they are in thousands, and impossible to manually sieve through.
Learn more about social media sentiment analysis.
An ML-driven platform for sentiment analysis to derive insights from customer or employee data gives you an advantage like none other. It effortlessly lets you track and measure customer satisfaction, notice product-market gaps, and thus even identify your ideal target audience. You get granular insights that can help you nurture customer loyalty and increase customer engagement. Alerts and notifications on your sentiment analysis platform also keep you ahead of any negative brand mentions that you can address timely.
Talk to our team and explore which sentiment aspect model is the best fit for your business because ensuring that your model is made for your particular industry is equally important. Extract insights from 23 languages, across data types, and from all data sources.