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How is Machine Learning Used in Repustate's Sentiment Analysis Tool?
Machine Learning (ML) is a branch of AI, which instead of relying on explicit pre-defined rules, actually studies the data it is analyzing and learns from it. The end result of training a machine learning (ML) algorithm is a model that understands the underlying data it was trained on. When this model is fed data, it will give a predictive answer based on the data that was used to train it. These models are what are used in Sentiment Analysis applications. Machine Learning models can be continuously fed data, which leads to an improvement in the accuracy of results over time.
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What are the Applications of a Sentiment Analysis Solution?
Below are some key applications of sentiment analysis solutions in important business areas today:
Patient Voice
By measuring patient experience with new Patient Voice methods, hospitals, pharmaceuticals firms, and health insurance companies can gain valuable insights. A study by the New England Journal of Medicine showed that 97% of doctors agreed that listening to a patient's voice was vital in improving patient care. Feedback and patient information can be taken through various methods including emails, online forms, and phone calls. Repustate's Audio Sentiment Analysis technology ensures that even if feedback is received over audio or video consultation records, you can still gain insights from it. Audio sentiment analysis analyzes the speaker's sentiment from speech signals by acquiring representative features called audio sentiment vectors (ASV). It utilizes automatic speech recognition (ASR) to convert speech into a transcript. That transcript is then fed into Repustate's text analytics pipeline to automatically discover emotionally relevant features from speech, providing you with vital, in-depth insights. Learn More
Social Media Listening
Social media monitoring is perhaps the most popular application of a sentiment analysis solution. Companies can monitor brand perception, by following the reviews and mentions across social media channels including posts, news, and comments. Repustate's sentiment analysis tool gathers information not only from textual formats on channels like Twitter, Facebook groups, or LinkedIn, but also from video uploads on platforms like TikTok, YouTube and Instagram Live. Social media monitoring allows you to identify areas that need your attention, and also to respond to individual mentions directly. In doing so, you can also build a relationship with an individual or social media influencer, as part of a long term marketing strategy. Learn more
Business Intelligence
Business Intelligence is the cornerstone of a thriving company. With Repustate's sentiment analysis tool, companies can identify hidden opportunities that would otherwise be unknown. Thanks to the AI-powered deep search technology in our software, you can dig through parts of the internet that are not otherwise accessible through a regular search. You can have access to influencer and competitor content. And with sentiment mining tools at your disposal, you can amplify your marketing and sales efforts, driving up your dividends. Business intelligence reports thus extracted, can help you in planning for the future, as well as help you assess the areas that need more time and attention. Learn more
Brand Insights
It is absolutely essential for a company to know what drives customer sentiment. What is it that works about its product or service, and what doesn't. Through sentiment mining tools, machine learning gives you the ability to know how your customers feel about the brand and effectively what they expect from you. Even if the feedback is given in the form of videos, Repustate's sentiment analysis tool, through its Video Content Analysis (VCA) capability, makes sure that you don't miss out on any important data. Enterprises can use these insights to proactively defuse negative sentiments and work on a more focussed branding strategy. Learn more
Business Reputation Management
Repustate's Sentiment Analysis tool allows you to find reputational gaps and manage their status. It uses NER and ABSA across multiple categories to give you insights as to what will result in a strong positive reputation holistically for your company. What an enterprise must realise is that their overall reputation is a sum of the reputation across its investors, executives, suppliers, employees, organizations it associates with, politicians it supports, and the demography it operates in. These factors are as important as the quality of their service or product, intellectual property, employee-employer relationships, and customer satisfaction. To grasp an actionable insight from opinions from various sources, and in various formats (video, textual, audio), is the invaluable advantage that a sentiment analysis application brings. Learn more
Competitive Analysis
Having an edge over your competitor means having all the information about them, their brand perception, their market share, and their market behavior, at your fingertips, in real time. Companies can work on audience engagement, contextualize and granulate key performance indicators, and build better messaging for their marketing and advertising campaigns. Spending millions on a campaign that does not have any real insight on the customer's pulse, is just bad decision making that is detrimental not only financially but also brand-perception wise. Sentiment mining applications eliminate this major miscalculation in judgment. Learn more
Opinion Mining
Opinion mining allows you to get to know a customer and focus on the polarity of their feelings (happy, confused, angry), intention (sale or no sale), and opinion. It tells you if the opinions are positive, negative or neutral. It even gives a finer view of that mined opinion based on a scale - Is the customer very unhappy, unhappy, or mildly inconvenienced? Opinion mining can help companies in market research tremendously, as well as in their marketing campaigns. Monitoring your social media round the clock 24/7 can give great dividends. You can reach out to influencers to promote or do damage control, and you can retrace your steps and check your track record from previous years and study the results. You can even target specific concerns a customer may have either in real time, or as a strategic long term decision. Learn more
Voice of Customer
When you focus on your customer, and actually listen to what they are saying, you find a powerful way to engage with them. Repustate's sentiment analysis solution uses the Text Analytics API to extract information from surveys, posts, emails, and other sources of data by discovering recurring themes and topics buried in that data that would otherwise go unnoticed. The solution helps you gain competitive intelligence and make fruitful decisions, whether it's in product quality, product segregation, or customer service. That Voice of Customer (VOC) must be measured regularly, goes without saying, because adapting to your market base is what ultimately decides the success of a business. Learn more
Voice of Employee
More and more companies are shedding archaic ways of collecting employee feedback and moving to more advanced technologies involving artificial intelligence, which mitigate human bias. Data is gathered using surveys and feedback that are ongoing during an employee's tenure at a company. These feedback programs include audio, chatbots, and videos. By applying aspect based sentiment analysis (ABSA), Repustate's sentiment mining tool gives companies powerful insights so they can establish a more substantial connection with their employees. The solution allows you more enlightened decision-making by helping you measure employee satisfaction and identify risk factors to thwart employee attrition by getting at the root of issues. Whether it's training and development, better work-life balance, improved management, or any other area, Repustate paves the way for you to re-imagine the employee-employer relationship. Learn more
The Repustate Sentiment Analysis Dashboard
With Repustate's proprietary and user-friendly customer sentiment dashboard, you can turn numbers into actionable insights. You can make your sentiment data come to life. To have a real impact on your business, your sentiment analysis strategy requires distinctive visualization strategies as your metrics are properly tracked and measured. With visual elements like charts, graphs, and tables, the Repustate dashboard gives you an easily accessible reporting feature, and you can instantly see and understand trends and patterns within the data sets gathered.
Sentiment Analysis in Multiple Languages
Sentiment analysis for text in multiple languages can be challenging if the underlying algorithms are relying on translating text into English first. Translations add another source for error and risk losing the nuance that exists in native languages. Multilingual sentiment analysis is indeed one of the most important features of sentiment analysis tools.
Repustate's Multilingual Sentiment Analysis Solution does not rely on translations in order to understand them. Native language support is accomplished through the use of native part-of-speech taggers, developed internally by Repustate, and by neural networks for each of the 23 languages. By analyzing each language natively, Repustate eliminates translation as a source of error in the full sentiment analysis process.
How is ML used in developing sentiment analysis applications?
All organizations collect vast amounts of data on an ongoing basis in various formats such as text, audio, and video. This massive horde of unstructured data, or Big Data, needs to be organized before it can be used for sentiment analysis or even for semantic search at an enterprise level.
Applications of Sentiment analysis solutions mostly use unsupervised learning - a machine learning task that is applied to analyze Big Data. Unsupervised learning classifies unstructured data - be it from any source (patient voice, sales and marketing collaterals, social media comments, reviews, videos, and podcasts) - into one or more clusters. In doing so, it organizes the data and can discover patterns that emerge from it.
Deep Learning is a subset of machine learning that is used to learn patterns in unstructured data. It uses neural networks (NN) - algorithms that mimic the way a human brain functions. They learn from the patterns, try to understand the context between words, and remember them for reference. NNs and deep learning are often used in speech and image recognition.
Supervised Learning, on the other hand, is used to categorize data based on predefined classifications. For example, surveys that have predetermined answers and require participants to check boxes. When you have the resources to manually annotate large amounts of data, supervised learning ends up being a faster way to arrive at a precisely trained ML model.
Once all the data is gathered, clustered in groups, and analyzed for patterns, ML algorithms process them to discover the emotions or sentiments buried in them. This sentiment analysis of the data unearths the underlying tone of a comment or review, whether it is negative, positive, or neutral.
How does Machine Learning make the Repustate Sentiment Analysis Applications smarter over time?
Repustate continuously gathers feedback from its customers to improve the accuracy of its models. Each incorrect classification and every inaccurate sentiment analysis tagging represents an opportunity to improve the machine learning algorithm's understanding of real world text. This continuous learning approach allows Repustate to create models that cover a wide range of industries through the use of Aspect-Based Sentiment Analysis.
Aspect-Based Sentiment Analysis (ABSA) breaks large pieces of text into smaller, semantically related chunks and then attributes feelings or sentiments to each chunk. And because Repustate's intricate solution uses unsupervised learning for aspect clustering, the more data it analyses, the more intelligent it becomes. It decodes the relationship between clusters of words, similar to how the human brain functions, using ingrained neural networks.
Below is an example of where "pretty" and "not spacious" are next to their relative aspect expressions of "view of the ocean" and "rooms".
This complex convergence of algorithms identifies and distinguishes which features of a product or service are liked and which are not, all within a matter of milliseconds. Sentiment analysis applications thus enable businesses like yours to gain meaningful insights from previously unstructured data. They empower you to strategize data driven tactics, create operational efficiencies, and overall competitive advantage.
Learn more about the other challenges in sentiment analysis and how ML helps in resolving them.