A Guide To AI Sentiment Analysis
You may hear someone say, “That was a nice sentiment,” often referring to a kind gesture. But the sentiment is present in the words we use as well. By understanding what sentiment is, how to analyze it, and how to apply the information, it may be possible to gain a competitive advantage. With AI’s growth and diversity, it is critical that we consider the specific applications of AI sentiment analysis and its limitations.
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If you are using, building, or working with AI for any reason or if you want to start such a project, it is critical to understand how to implement AI sentiment analysis into what you are working toward. Without understanding sentiment, the data AI is providing to you is not as accurate and comprehensive as it could be, and it could steer you in the wrong direction. Let’s take a closer look at what it is and what applications it applies to your business.
What Is Artificial Intelligence Sentiment Analysis?
AI sentiment analysis is the process of using artificial intelligence coupled with natural language processing (NLP) to analyze text with the specific objective of understanding its emotional tone. It is not the process of capturing data, nor does it focus on relaying facts. Rather, the process focuses on understanding emotions, feelings, and beliefs conveyed within the text.
The objective of artificial intelligence text analysis is to create actionable insights—to provide your business with information it can use to achieve its objectives. To do this, the process must provide an answer: Is the text positive, negative, or neutral? By understanding this categorization, it is possible to utilize that data for various purposes.
Another way to look at this process is that it enables you to analyze digital text to determine its emotional tone. Sometimes called opinion mining, this process enables a company to better understand what people feel rather than just what they say.
AI Sentiment Business Use Examples
Why go through the trouble of AI sentiment analysis? There are several factors to consider. First, as your company grows, you’ll have far more than a handful of bits of information online about your business. You want to know the full story – all of the details about it. There is no way for a human to be able to capture all the data out there, analyze it individually, and make decisions from it. Yet, with AI sentiment analysis, it is possible to capture more information, providing a better, broader scope of data that you can then use to make decisions.
The second factor to consider about AI sentiment analysis is what you can do with this information. Consider some of the types of AI analysis text you can capture and use:
- Customer reviews
- Customer service experiences in chats
- Emails and other correspondence
- Online surveys
- Brand mentions
- Customer feedback on a specific product or service
- Brand management
- Social media monitoring
- Market research
With artificial intelligence sentiment analysis, your business can better understand what information is out there that could be influencing the decisions others make about your business.
AI for Sentiment Analysis Techniques
Using AI for sentiment analysis is a profound resource to help you build a better understanding of data that is critical to your business. It is critical to understand how it works. To do that, we need to consider the AI-driven sentiment analysis techniques used. Here are a few examples that you may need to consider.
Lexicon-based methods: Often referred to as knowledge-based approaches, lexicon-based methods are pre-developed and designed manually. They analyze semantic and syntactic patterns either by generating a dictionary by tagging words or considering syntactic patterns.
A range of methods fall into this category. One is the corpus-based approach, which will use the analysis of semantic and syntactic patterns in large text datasets to facilitate an understanding of sentiment within sentences. It begins with a predefined sentiment term and then expands on this over time by learning from patterns. A second example is a dictionary-based method that uses a manually created list of sentiment words. It then expands on this by finding synonyms and antonyms for lexical resources. In either strategy, lexicon-based methods are easily accessible to the public, and they tend to be less expensive because you do not need to implement advanced algorithms. There is also no need for data training.
Automated and machine learning methods: A secondary artificial intelligence text analysis approach uses ML algorithms to categorize sentiment based on statistical models. The sentences are then transformed into vector space to apply ML algorithms. There are several strategies that fall under this method.
Naïve bayes (NB) is one example. It is a supervised classification method used for feature extraction. It assumes each token (or feature) is independent of others. That way, it can navigate factors such as grammar mistakes or spelling errors. Support vector machine (SVM) is another automated option. It works by identifying optimal decision boundaries so it can separate various classes within data. This method is often very effective in classifying sentiment across more than one dataset.
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Logistic regression, which uses a weighted sum of input features to classify data into binary categories, and decision trees, which will create a tree-like model of decisions based on the possible consequences of categorizing sentiment, are two additional strategies. No matter which of these or other types of automated or machine learning strategies you use, there are key benefits, including that you can train them to detect sarcasm and irony. They can also be trained in the negation of sentiment analysis. This makes it an essential option for AI sentiment analysis on social media. Also, note that it is possible to learn the affective valence of the words because there is no pre-determined dataset. Typically, these methods are faster and offer more accurate results.
Hybrid Approaches: When we consider AI sentiment analysis, it is possible to use more than one strategy and methodology rather than just automated or lexicon-based methods. Often, both of these strategies have limitations that could influence decisions. That is why it is often beneficial to consider a hybrid approach.
Some companies will implement a hybrid approach that will include lexicon-based methods along with automated strategies. This can help both programs to provide accurate information – and it enables one tool to overcome the limitations and flaws of the other. The objective is to have an overall more accurate process.
Aspect-Based Sentiment Analysis: Another approach often applied is called the aspect-based sentiment approach or ABSA. It has three specific stages for application: aspect detection, sentiment categorization, and aggregation. With this process, aspects – or specific data points of interest – are identified utilizing machine learning as well as natural language processing (NLP) techniques. This allows for the determination of sentiment polarity. Then, the results are aggregated for a more comprehensive analysis.
Transfer Learning: Yet another AI sentiment analysis tool and method is called transfer learning, and it is more advanced but becoming more accessible. It uses pre-trained models of knowledge that are adapted to a new task. The process reduces a significant amount of the training necessary for most applications. In short, transfer learning models, including BERT-based models, are more accurate and efficient and require less training because they reuse learned features from one model to the next, building upon that information over time.
The Importance of AI Analysis Text Refinement
Businesses can leverage sentiment analysis for a great number of opportunities. The bottom line is that it can nearly always be used to make better decisions within the business. That could include improving customer experience or pinpointing trends as they develop. Yet, there are challenges that must be considered in this process. AI sentiment is not always as direct and as effective as it can seem to be.
Language is evolving rather than static. The way things are said, the implications of words and phrases, and even the creation of new words can throw off any type of AI analysis of text if this is not carefully considered as a component of the process.
One example of this is the use of language nuances and slang. Many people today could become quite concerned reading the text messages of a teen — the language, terms, words, and even emojis and spellings – of statements are all different. Looking beyond that, those in their 20s are likely using very different wording and nuances than someone in their 50s and so on. Age is just one factor. Others include location, culture, and a wide range of other factors.
This matters because AI sentiment analysis is only accurate when you see the full picture and all of the details. Yet, it cannot provide accuracy across all demographics and areas without a consistent way to grow and advance.
To solve this, we need to focus on continuous model training. Note that for all types of AI, model training is valuable. It is the way in which we feed the AI dataset with information so that it can learn from it. This is not a one time process like downloading a manual for how to use an electronic device. Instead, it is a way of learning, refining, and updating over time as those language nuances change and grow.
More so, there is a need to refine it. To achieve optimal performance, using artificial intelligence text analysis becomes critical after it is refined with human influence. This allows for a better understanding of what is truly being conveyed.
Also note that in order for AI to provide accurate information, it must be able to evaluate incoming data – not just the same dataset every time. Analyzing customer reviews is only helpful if you can analyze new data and new reviews as they appear. Otherwise, you can only learn from that one exposure. Sentiment analysis should never be a snapshot of a pre-compiled data set if you are using it to make business decisions. Instead, it needs to offer real-time applications that allow you to monitor sentiment as time goes on.
How to Implement Sentiment Analysis with AI
Now that you have a good idea of what sentiment analysis is, how can you begin using it as a component of your business operations? You can apply it in several ways, but let’s start at the top to create a way to capture information.
Capturing information is critical. The first step is to capture the information you want to analyze. There are AI tools that will do this for you, but you need to consider real-time, up-to-date information. This is where web scraping provides a resource. Utilizing web scraping and proxy services are core components of ensuring this. Take a look at the process you can expect:
- Implement a web scraping API or build your own web scraper. The goal here is to use this tool to properly capture information that is important and relevant to your decisions and steps from available resources. You can learn more about building your own web scraper in Python or using one of our many other guides to help you with this process.
- Establish the use of proxies. A proxy service provides you with a way to protect your IP address—and, therefore, your identity—from target websites that may want to limit your access.
By applying these two components to your AI sentiment analysis tools or strategies, you gain the ability to create highly accurate and detailed insights that you can then apply to your business. You can then make decisions based on the emotions that consumers are expressing about your brand, product, service, or other areas. Dive into what AI sentiment analysis can offer you by using the resources provided at Rayobyte. We have developed guides and tools to help you every step of the way. You’ll find both a web scraping API and the necessary proxies for web scraping available to help you. Contact us now.
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