A Guide to Opinion Mining
Opinion mining and sentiment analysis are powerful tools that enable businesses of all types to gain better insight into their customers, make decisions, and research data.
With opinion mining, it’s possible to capture critical information for key business decision-making. As a business, you need to stay on top of your company’s brand, customer sentiment, industry trends, and other individual factors critical to remaining competitive. With this type of tool, you can do that.
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To help you learn what opinion mining and sentiment analysis are and how they can be a direct benefit to your business, we have built this guide. Not only are we going to cover what these tools are, but also how you can make the most out of them even if you have not previously used AI.
What Are Opinion Mining and Sentiment Analysis?
How can a business or researcher gather and analyze public opinions from data it has without having to sit down and read through every review and comment left? That is what opinion mining can do.
Opinion mining, which is a subset of text mining and sentiment analysis, interchangeably referred to as such, is a tool that uses Natural Language Processing ((NLP) and machine learning to take data and analyze the content to assign it a sentiment. This process determines if a statement is positive, negative, or neutral, allowing a business to determine what its customers think and feel about it.
Whether its an overall brand sentiment analysis or customer sentiment analysis for specific products or topics, a thorough understanding of the emotional subtext is critical. A simple review of a customer could seem positive only to have been written in a sarcastic tone. Other times, elements like emojis or misspellings can create challenges for simple customer sentiment tools to truly understand what is being said in the review or customer data. With opinion mining, it’s possible to use a tool, one that functions using artificial intelligence, to better understand the emotions, thoughts, and feelings a customer has within the written text provided.
Opinion mining uses NLP, text analysis tools, linguistics, and biometrics to better understand a piece of text’s opinions and beliefs. It quantifies and studies the affective states and subjective information that you need to know as a business owner to truly understand your customer’s sentiment. This is done through a combination of tools, some of which we will discuss in a moment.
How Can Brands Use Opinion Analysis Like This?
With AI sentiment analysis and opinion mining, a tool can take the data you have extracted from social media, news publications, and other resources and analyze it, categorizing it as positive, neutral, or negative. It then can provide insights into whether the emotions and attitudes expressed within customer reviews and other data points are positive or negative, which is an opportunity for businesses to react.
Take into consideration the wide range of using opinion mining within business. There is virtually no limit on the way you can use this process. Keep in mind that, along with all other forms of AI, you should only approach these tasks using ethical strategies and with ethical intentions. Some of the most common uses for opinion mining and sentiment analysis include:
Determine Customer Segments: One of the most important tools for using sentiment analysis is to analyze customer satisfaction. With that process, you can then determine which customers feel negatively about your brand – for any reason – and that could help you to identify which customers need more support in the buying journey. You may also identify customers who are your brand ambassadors and benefit from additional support.
Make Product Improvements: You can also use brand sentiment as a tool to gauge what you need to do next to push your business to the next level. For example, you can use sentiment analysis to analyze what people think about your last improvement or product release and use that to inform your future decisions. It can help you determine which features are most important to your business.
Handle Customer Service Concerns: A large portion of returning customers is due to the quality of the service they receive. By using opinion mining, it is possible to prioritize quality customer service within your business. Whether they are submitting a ticket for a helpdesk need or they are purchasing a product over the phone with customer support, you need to know how well your team is doing.
Monitor Competitor Sentiment: Use customer sentiment as a tool for analyzing your competitors as well. You can apply the same technology and strategies to your competitors as you do to yourself, which gives you insight into how well customers needs are met by your direct competitors.
Improve Marketing Strategies: Opinion mining can help you determine what topics or details are most important to your customers. This allows you to modernize your marketing to address those specific areas, which could help ensure you are getting the best ROI possible for your project.
Build Your Brand: Customer sentiment is a core component of understanding your brand reputation. Above and beyond online brand monitoring, it is one of the best tools for helping you to see what the real world thinks about your company and how well they align your company with their needs. Your brand is an incredibly valuable asset and should be treated as such with careful focus and monitoring.
With so many opportunities to use sentiment analysis and opinion mining, you may be ready to jump on board and start using it. Yet, there is more to the process that you should take into consideration, including how to put in place a system that allows you to maximize your ability to capture this valuable information.
How Sentiment Analysis Opinion Mining Works
Your business wants to capture information, such as customer reviews or statements made about your company by customers or third-parties. To do this, you will need to work through several steps.
Getting the Data You Need to Analyze: The first step is to capture the data you need to analyze. There is no shortage of information available online about most companies, even smaller organizations. However, it is spread out across numerous resources. Your objective, then, is to determine what data you want to capture first. This could include social media posts, customer reviews, statements in forums, news article details, or other information that notes your business, products, services, or other important information you want to follow.
Then, you need to capture that information. The simplest and most direct method to doing so is to use an API, such as a web scraping API, that you can set up to include the specific details you want to capture. Rayobyte’s web scraping API is one of the simplest solutions to use to get started.
Many companies benefit from the use of web scraping tools. You can build your own using a variety of tutorials we have created for you to use. Check out web scraping with Python to get started, for example. Web scraping is the process of creating a tool that visits the website that you need to capture information from, parses through the HTML content on that page to find the specific terms and mentions you need, and then captures that data and places it into a file you can later analyze. Various tools are available to help you to do this.
A third option is to have direct surveys completed of your customers. You can invest in this type of service to help you capture data about your customers’ opinions and thoughts, such as after they make a purchase from you. However, this may not provide you with as much information and detail as you may get from web scraping instead. That’s because, with surveys, you have to get people to buy in and take the survey, whereas online reviews tend to be easier to obtain since people believe they are helping the general public rather than informing the company.
Text Preprocessing. Once you have the information you need for opinion mining, you need to read that raw data to be useful. This can be easy to do when you use one of several valuable tools to help you. The objective is to remove anything from the data to allow you to have a very simplified process of analyzing data. Specifically, these tools will work to apply tokenization, stop word removal, and lemmatization. Tokenization enables you to replace key sensitive information within a review with a token. Neither the token nor the original sensitive information influences the sentiment. Stop word removal helps to increase the efficiency of the process by utilizing the removal of words like “a” and “the” from the content. Finally, lemmatization works to associate various forms of a word together, such as various tenses, helping to improve understanding of content.
At this point, you’ll likely find there are many customer sentiment analysis tools out there. We recommend the following:
- NLTK: Sentiment analysis with this tool can help with these areas and provide a simple-to-use design.
- TextBlob: TextBlob is a Python library that can provide processing of textual data. It helps to support NLP. It is also an efficient and easy tool to use.
- SpaCy: A third option is a pipeline component that analyzes content to simplify English sentences.
Sentiment Classification: With emotional intelligence + opinion mining, the objective is to capture more accurate insight into what people believe about your product or service. Sentiment analysis classification allows this to happen. There are several specific tools that can be used to achieve this goal. The key steps we encourage you to take are lexicon-based methods, like the use of VADER, or machine learning models like Naïve Bayes and SVM. You can also use deep learning approaches, which are a more cutting-edge and advanced strategy. These include GPT models and BERT models.
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No matter which method you use – and it is really about a preference – the goal will be to have this information classified by sentiment, such as positive, negative, or neutral. This allows for you to begin analyzing what people really believe through the emotions they express in the content.
Deeper Learning. For most businesses to achieve the best possible insight, it is beneficial to utilize Aspect-Based Sentiment Analysis or ABSA. This is a final step that allows you to capture more information about specific products or features. Instead of just collecting data about a specific company, for example, you can get specific insights into customer service, product features, or specific elements that make your brand recognizable. This type of strategy provides a deeper model for understanding sentiment.
Sentiment analysis, opinion mining, and all of these strategies are meant to support your business’s efforts to understand products and services better. There are various ways to use these tools but the ultimately objective is to use opinion mining as a way to capture valuable information about your business – information that is already out there.
Put Sentiment Analysis and Opinion Mining to Work for You
Utilizing sentiment analysis and opinion mining lets your business track customer sentiment. It aids in improving the marketing campaign reach you have and how effective your investment is. It can also aid in supporting your business decisions, whether that relates to new product launches or better feature development. It could also help you with customer engagement by creating a way to reach your customers in a method they appreciate.
Brand reputation management is very important for most companies. With sentiment analysis, companies can better understand customers’ emotions, thoughts, and feelings about their brand. What’s more, this entire process can be entrusted to automated sentiment analysis tools, which means you do not have to do all of the work yourself.
To learn more about how sentiment analysis and opinion mining can help your business, check out the tools and tutorials at Rayobyte. You can also use our services to web scrape your content and analyze it.
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