In the digital age where products and services are showcased on a variety of online platforms, sentiment analysis models allow businesses “to understand emotional tones in speech and print” using artificial intelligence. It is essential for all businesses to understand the emotional response of the market to their product, whether it be positive, negative, or neutral. This helps businesses generate large amounts of data research that management teams can then use to understand customer requirements and trends. Sentiment analysis allows businesses to figure out what the business is doing well, and not so well. The resulting information can be extremely valuable.
With the growth of AI, sentiment analysis models have become a popular tool for many businesses. This has allowed them to sift through large volumes of data to understand emotions and reactions towards their brand which otherwise would be physically impossible.
There are still business functions where AI is severely underdeveloped, because although current sentiment analysis models make it easier to analyze data, they are not actually helping businesses get on top of big data problems.
In this article, we will dive in deeper to explain how sentiment analysis models operate and why they fail to help many businesses.
What is Sentiment Analysis?
Sentiment analysis is typically used for social media analysis, survey research, and the tracking of psychological trends. Sentiment analysis models scan “social media posts, tweets, and online product reviews, as a way to track opinions, reactions, and ultimately improve customer service and experience”.
Sentiment analysis, also known as opinion mining, is a technique used to determine whether the data in these posts is positive, negative, or neutral. The model is used to interpret textual data and helps the business monitor how well the product is being received by customers. Brandwatch provides a deeper dive into sentiment analysis, how these analyses work, and how they are then used by businesses to understand and monitor the conversations around their brand.
These days, customers can express themselves freely on a wide range of platforms online, which is valuable data to many businesses. Being able to interpret this data and how customers perceive the brand can be essential for business planning and strategy in the future.
By using sentiment analysis models, businesses can analyze 5000+ reviews about their product to help discover if their customers are satisfied with the pricing plans or customer service. This allows businesses to update their marketing approach, and even contact unhappy customers immediately on social media platforms if necessary.
Types of Sentiment Analysis Models
Apart from being used to detect emotions, sentiment analysis models can also be used to detect urgency and even intentions. Depending on how businesses want to analyze customer needs, they can clearly define the category and analyze it accordingly.
The following are some of the most popular forms of sentiment analysis models:
Fine-grained Sentiment Analysis
Polarity precision is important for many businesses, and with fine-grained sentiment analysis, the AI can group data into the following categories to better understand their customers:
- Very positive
- Positive neutral
- Negative
- Very negative
A fine-grained sentiment analysis, according to Prashanth Rao, “can provide more precise results to an automated system that prioritizes addressing customer complaints”. Businesses within this sentiment analysis model will then categorize the “Very Positive” responses as 5 stars, and “Very Negative” as 1 star.
Detecting Emotions
This is another type of sentiment analysis where businesses “detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise”. However, with detecting emotions, businesses need to be careful as emotions can be interpreted incorrectly.
As an example, ‘bad’ or ‘kill’ can be translated to “the product is really bad”, or it can be treated as “the product is so bad-ass” which would refer to a positive emotion. With this analysis, an inaccurate picture of the emotion can be recorded.
Aspect-based Sentiment Analysis
If a business wants to analyze sentiments of texts such as product reviews, they will want to know exactly which aspects or features are being mentioned in a positive, neutral or negative way.
Aspect-based sentiment analysis models help in this way as it determines if this sentence: “the battery life of this camera is too short” indicates a positive or negative opinion.
Multilingual Sentiment Analysis
According to Nicholas Bianchi at Repustate, multilingual sentiment analysis can be more difficult to understand than other types of sentiment analysis as it involves understanding “complexities of language in general, and the specific differences between various languages”. These resources are available online in the form of sentiment lexicons, while others are created using algorithms or machine learning.
How is Sentiment Analysis Evolving with AI?
Sentiment analysis has become important in the digital world as it helps businesses understand their customers’ opinions in a faster and more accurate manner. However, as businesses enjoy the advancements of evolving sentiment analysis models, there still appears to be a cap when meeting the demand of larger corporations.
Think about it this way, if you’re an owner of a small business and receive close to 20 responses via surveys you sent out by email in the past month, you can probably analyze it yourself. However, if you are receiving 20,000 responses via email, you will be unable to read or analyze this data manually in the same way.
This is where AI comes in handy with sentiment analysis and uses AI to detect emotions in the text by using Natural Language Processing or Machine Learning.
Natural Language Processing: Natural language processing transforms human language into something machines can understand. It uses both syntactic techniques (which helps it understand the structure of the text) and semantic techniques (which help understand the meaning of the text). Once NLP processes the text, it is then used by Machine learning.
Machine Learning: Machine learning allows machines to understand the patterns in data and make predictions. It doesn’t rely on explicit instructions. To work effectively, the business needs to train it by pointing out the examples of emotions in texts.
Challenges with Sentiment Analysis Models
Although the use of AI has encouraged sentiment analysis models to grow, the fact remains that it still poses a challenge to many businesses. According to Rudolf Eremyan at Toptal, “there are a lot of challenging problems which seriously affect sentiment analysis accuracy”. It is clear that data analysts still have lots of manual work to do before AI can successfully take over and interpret sentiment analysis models.
Below are some challenges and reasons why current sentiment analysis models aren’t working for many businesses with big data:
Sarcasm Detection
Sarcastic texts can be a problem for AI to identify. It is very common for people to express their negative sentiments using positive words, and sentiment analysis models in AI are unable to identify the intended meaning. In many cases, the text can cheat the AI when analyzing sarcastic text.
Sarcasm is frequently found in user-generated content such as Facebook comments, tweets, and other social media posts. Sarcasm detection is challenging for sentiment analysis models on these platforms and offers many issues for businesses wishing to analyze social media posts. Furthermore, there is a continuous variation of words used in social media posts, which then makes it more difficult to train AI for sentiment analysis.
In addition to linguistically defined sarcasm, researchers have come up with another type of sarcasm called numerical sarcasm. The concept behind that is by changing the numbers affects the polarity of the text. An example:
- It’s 30°+ outside, and I am sweating (Non-sarcastic)
- It’s -30° outside, and I am sweating (Sarcastic)
As a result of changing the numerical values, the meaning of the text has changed. Different AI approaches are being used to help understand data like this for correct recording. These approaches include rule-based, statistical, deep learning, and machine learning. However, despite researchers’ best efforts, it seems that larger volumes of big data are not processed properly by AI which in turn causes many big data issues for larger businesses.
Negation Detection
Another challenge that is affecting the polarity of words, phrases, and even sentences is the use of negation. Linguistics uses different rules to understand if the negation is occurring, but these rules cannot be understood by AI efficiently. The way AI uses it is by negating all the words from a negation cue to the next punctuation token. This leaves many errors at the end of the process when trying to accurately analyze the data.
Emojis
There are really two types of emojis that exist, one known as the western emoji and is made from one or two characters. The other, the eastern emoji that is made from a longer combination of characters. This is important to note, especially in tweets, where a lot of preprocessing is needed to understand these characters. This is where the current sentiment analysis models fail in AI as they are not able to distinguish between the two.
Word Ambiguity
This is another pitfall that businesses that work with sentiment analysis models face. The problem occurs as it can be impossible to define polarity in advance. The polarity for some words is strongly dependent on the context they are written in.
An example of a word ambiguity challenge with sentiment analysis models would be:
- The story is unpredictable.
- The internet connection is unpredictable.
The above two examples indicate just how word ambiguity plays out. In the first example, ‘the story is unpredictable’ indicates a positive polarity while in the second case, the internet connection indicates negative polarity. Once again, it is not clear and AI relying on machine tactics is unable to give a clear picture, which then prevents an accurate picture.
Multipolarity
Multipolarity is another problem that arises with sentiment analysis models in AI and it can become increasingly difficult to pick a context when multiple products are being talked about at one time. For instance, just how taking out an average can hide valuable information, doing a total analysis of a text which talks about multiple products can also be a problem. Ideally, what should be done is that each sentence is analyzed differently and in a given context. However, even with AI, such sentiment analysis is just not possible, and as a result, a better picture cannot be extracted.
Financial Settings
As sentiment analysis has evolved, many organizations are implementing the technology for monitoring comments or tweets to improve the customer experience. AI professionals and business leaders are overlooking other equally valuable use-cases for sentiment analysis, most notably with analyzing financials.
Sentiment analysis in AI can be used in financial settings in places other than automated trading systems. Analysts often use sentiment analysis models to review transactions, but in these instances, the models are only looking at financial numbers to form conclusions. This leaves the door open for numbers to be overpowered by valuable information in news articles, press releases, and other sources that could help in financial analysis.
Companies such as Alphasense are already incorporating this with the use of NLP. The company strives to change “business information [the way] Google did for the internet,” says CEO Jack Kokko. “We’re organizing it and making it really fast and easy to find the information you need as a business professional.” Platforms and tools such as this one are helping analysts dig deeper into the information, better inform strategic and financial decisions, and reach more comprehensive conclusions.
Final Thoughts
Current sentiment analysis models have rapidly evolved over recent years with many businesses using them to help understand their customers on a deeper level. However, there is a fair argument that the majority of existing sentiment analysis models are solely used for surface-level analysis on social media conversations and customer reviews.
For larger global corporations, the current sentiment analysis models in AI just don’t offer the help they require when faced with larger data volumes. Articles relating to large global companies are plastered all over the internet, and the current sentiment analysis models are unable to yet provide a way to stay on top of all of them. This is where these models fail large businesses as they are unable to capture all data effectively in a way that is needed.
