How To Implement Real Time Sentiment Analysis
Imagine a customer purchasing a product from you, becoming upset about it, and going to their social media to share their bad experience. Once they do, that message takes off, and soon, you are on the bad side of a viral video.
With real time sentiment analysis in place, you could learn about that risk sooner, take action to fix the problem to prevent reputational harm, and be able to save that customer (and many others after them).
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To quickly recap, sentiment analysis is the process of capturing valuable information about your business, applying automation to analyze it, and then learning all positive, negative, and neutral feedback so you can take action.
With real time sentiment analysis is a step above automated sentiment analysis, collecting and utilizing data faster and more efficiently so you can apply that information to the decisions you plan to make. Let’s talk about how sentiment analysis can make a difference in the way you operate your business.
The Basics of Real Time Sentiment Analysis
Every business can benefit from the use of sentiment analysis. It is a process that involves the use of natural language processing (NLP) and machine learning techniques. These are some of the most advanced tools available today for not just capturing information but analyzing that information.
The objective is for the tools to take your data and analyze it, putting it into a category based on whether the tool determines that information is positive, negative, or neutral. What makes real time sentiment analysis different is that it does this with up-to-the-minute precision. That is incredible in terms of the wide range of resources that it can provide your business.
With sentiment analysis in real time, you can act faster when there is a crisis or gain more insight into how well your product and marketing campaigns are operating again. This enables a business to stay ahead of the competition, monitor the market for changes, and ensure it is making wise business decisions.
How to Put Real-Time Sentiment Analysis Into Place
In traditional sentiment analysis, your business feeds information to the tool for analysis. For example, you may use web scraping in Python to create a robust tool to capture all of your company’s brand mentions. You then provide that scraped data to the sentiment analysis tool to categorize the information. With real time sentiment analysis, the process requires the use of data streaming, a way for you to capture information in real time. To help you see how you can easily set up this process with the right tools, consider the following steps.
Obtain Data Streaming Sources
Data streaming is the process of capturing high volumes of continuous information delivered in incremental manners. This way, it enables low-latency processing. In other words, data streaming allows you to have a constant flow of data to process real time and creating an ability to analyze data and gain insights from it immediately.
This information can be generalized, or you can be quite granular and specific, depending on your needs. So let’s start with where these data streams come from: the source.
You could, for example, use sources like X API (for the social media platform formerly known as Twitter), customer feedback platforms or even your own live chat systems. But each has their advantages and disadvantages.
- The X API enables programmatic access to X but it can be restrictive, especially at lower pricing levels.
- Customer feedback platforms are great, but they represent a channel where customers want to talk to you. If you want to fully integrate customer sentiment analysis, you need to know what they’re saying elsewhere.
- Live chat systems, like the above, are also a great source of information. The conversational data within can be integrated with sentiment analysis tools to see how your brand appreciates or depreciates as customers try to solve problems – but like feedback platforms, it’s only part of the wider picture.
These sources can be a good place to start, but if you’re serious about understanding your public sentiment, you need a much wider net. Scraping for online brand mentions, for example, can find sites where your company or its products and services are being discussed, and then you can use tools to assess how this sentiment is changing in real-time.
No matter which of these methods you decide to use, the goal is to have the most robust solution for capturing data that is valuable to your business. Do some research to determine where your customers leave notices and how they communicate their experiences about your product in particular. That often includes social media but may include chat messages, online blog posts, news insights, and much more.
Preprocessing Methods
Preprocessing is when all this raw data gets cleaned up. With the information available to you through data streaming, there will be a great deal of ineffective and non-useful information. The process of using real time sentiment analysis, though, requires efficiency. By applying the following three strategies, it is possible to minimize those risks and cut out a lot of the frustration that often comes with it.
- Tokenization: There is nearly always a need to use tokenization, a process that replaces specific words and phrases that could be sensitive information with tokens that mean nothing. That is, the sensitive information, such as names, are removed and replaced with other data that does not change the sentiment of the process but does protect the privacy of the user or otherwise minimizes distraction.
- Stop word removal: Stop words are all types of small words, such as pronouns and other details that do not really contribute to the conversation. For example, words like “the” and “and” as well as “a” do not contribute to the sentiment of the content. Stop word removal gets rid of those confusing elements to make the process easier to manage.
- Lemmatization: The lemmatization process lumps together words that are spelled differently but that have the same meaning. This could include, for example, lumping together different tenses of a word.
Each of these three steps helps to make the data collected easier to navigate. So, how do you quickly navigate that process for real time sentiment analysis? You use tools like these:
- NLTK: Sentiment analysis using NLTK helps with all of these areas and provides a simple-to-use design. The interface is easy to use and you can automate the process.
- SpaCy: With SpaCy, you get a pipeline component that analyzes content to simplify English sentences.
With such large volumes of data, you’ve probably already figured out that AI sentiment analysis is the only way to go – in a pinch, you can also use ChatGPT for Sentiment Analysis. However, at this stage, a more robust natural language processing tool like NLTK or SpaCy will deliver the best results.
Now that you have a wide range of data to analyze, it is possible to start gaining valuable information for it.
Sentiment Classification
Sentiment classification is the process of actually assessing the information in order to make a decision about whether it is positive, negative, or neutral. There are many ways to do this, but there are several core tools that we believe are some of the best for ensuring you get high-quality content.
- VADER: VADER works well for short text, such as social media posts or a direct message. It is a pre-trained model that will be easy to implement. It will take the information you provide and categorize it quickly.
- TextBlob: Another tool for this task is TextBlob. It is also a type of pre-trained model that has been built for tasks like this. It has an easy-to-use interface and can be applied in a variety of strategies to provide authentic information and analysis.
- BERT: BERT is a transformer-based deep learning model created by Google. It is quite robust in understanding the context of text and does this by analyzing bidirectional relationships between words. Because of its versatility, BERT is one of the most sought-after tools.
- Apache Kafka: Apache Kafka is an excellent choice because it is built to handle the constant flow of data that will be coming into your system with real time sentiment analysis. This is an open-sourced, distributed event streaming platform that is used by a wide range of providers. It delivers excellent high-performance data pipelines and streaming analytics.
- WebSockets: WebSockets is a protocol that allows for two-way interactive communication between the server and the browser. You can implement it by enabling bidirectional communication to process and analyze data streams.
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Dashboard Applications
This data has gone through a significant amount of insight and research to this point – all of which is done with real-time accuracy and speed. However, to make that information more usable and robust, you need to use a dashboard. There are various dashboards out there, but the objective of any of them is to visualize the content that you are creating. That is, these dashboards take the data and create a visual depiction of it, allowing you to clearly see peaks and valleys and areas of opportunity.
For real time sentiment analysis in data analytics, we encourage you to use the following dashboards for the best overall result:
- Plotly: Plotly offers a variety of solutions to create customized dashboards for this task. You can choose what works for your specific needs, using tools like heatmaps, bubble charts, and interactive reports to make that data come to life.
- Streamlit: Another nice tool for displaying information like this is Streamlit. It allows you to transform Python scripts into interactive content that you can use for a variety of tasks. It also does this in a matter of minutes, including building custom dashboards for your application needs.
- Power BI: For those who are looking for a Microsoft solution, Power BI can work well as a data visualizer. It is a unified and scalable model that allows you to connect to your data and then visualize it in a matter of moments. It is one of the most advanced solutions but what makes it ideal is that it is easy to put in place to create captivating visuals that are easy to interact with in real time.
What Realtime Sentiment Analysis Means to Your Business
The utilization of real time sentiment analysis empowers your business to react. There is a constant level of information coming into the internet today from social media, news outlets, and dozens of other platforms. That information is both helpful and potentially hurtful if the data is not applied in the best possible manner. With the use of these streamlined applications, you can create a way to monitor for the good and bad so you can react to it sooner.
For more information on what to do with this information, you can read our guide on “how can marketers use sentiment analysis?“.
It is critical to put in place tools that work for your needs. If you are looking for a way to develop your own AI web scraping solution, we recommend checking out all of our robust resources and tutorials available to you online. What’s more, you can also use Rayobyte’s web scraping API to help you navigate information immediately.
Also, note that it is important to protect yourself throughout this process. That is why we recommend using proxy services.
A proxy helps to block access to your IP address, which means the target website you are pulling information from is not able to block you or track your actions. This improves the outcome of your processes and enables you to achieve a better overall outcome. To remain competitive, companies have to be able to react to information, reviews, social media posts, and data bites about your company fast, and that means you need a way to have real time sentiment analysis in place. Utilizing the services and tools here and following our steps, you will be able to monitor your customers’ emotions and sentiments behind the reviews and statements they make.
This can change the way you do business. Contact Rayobyte to learn more about the process and how we can help you.
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