Seasoned marketers still remember how businesses used to gauge consumers’ sentiments through surveys, customer service feedback, and relying on focus groups. These methods are still valuable but they are limited in scale and involve a lot of manpower to process data.
Fast-forward to the beginning of the 21st century. In the digital age, people vent their frustration or admiration on review sites and social media. A single social media post can reach millions of users encouraging or discouraging them to engage with a particular product.
The good news is that brands no longer have to organize surveys and polls to know how people feel about their services and products. The bad news is that people speak freely their minds whether they praise or curse someone or something. On top of that, there’s so much data that marketing teams can’t handle it by themselves.
This is where AI-driven sentiment analysis enters the stage. It is the most efficient and fast way to monitor audience reactions and emotions. It is the latest secret weapon for many companies for crisis management and product improvement. But! Yes, there’s always a but! AI sentiment analysis isn’t perfect and has its own limitations.
In this article, we will explore the major types of sentiment analysis, how artificial intelligence can enhance evaluation, and what areas of sentiment analysis can present some challenges that AI can't fully solve.
What Is Sentiment Analysis?
Sentiment analysis, a natural language processing technique, specifies the emotional tone of a text. In simple terms, it helps to understand whether a piece of text conveys negative, positive, or neutral emotions. In influencer marketing, brands use sentiment analysis to measure people’s feelings about influencers’ content, products, services, certain topics, and interests.
Sentiment analysis has become one of the most imperative methods to understand how the public relates to a business or a product. For brands, it is one of the most helpful tactics to preserve their credibility, protect their reputation, and improve their product range.
5 major types of sentiment analysis
Polarity sentiment analysis
This is the most basic type of sentiment analysis. It divides audience sentiments into 3 groups: positive, negative, and neutral. For example: “We had a great time in your hotel. The room was spacious and light and very cozy.” This is a positive sentiment. However, this one is a negative: “The room was dirty and the staff rude.” A neutral sentiment would be something like this: “We booked a standard room in your hotel.”
Fine-grained sentiment analysis
Fine-grained is the advanced iteration of the polarity sentiment analysis. Instead of three sentiments, it is enhanced into five: very positive, positive, neutral, negative, or very negative. It may use a 1-10 scale or a five-star rating method and assign the scores to one of the 5 sentiments. In this sense, a four-star rating means a moderately positive emotion.
Emotion detection
Instead of classifying sentiments into the previous umbrella terms, emotion detection tries to identify particular emotions in the text, such as anger, sadness, or joy. This analysis type enables brands to comprehend people’s emotional level toward their brand or product. As an example, a review like “What a waste of money! Don’t ever order from them!” triggered disappointment and anger, but “I’m absolutely surprised in a good way!” generated joy and surprise.
Aspect-based sentiment analysis
Instead of assessing the overall sentiment associated with a product or service, aspect-based sentiment analysis gauges user emotions expressed toward specific features. For example, a sports brand may learn from users’ reviews that their leggings absorb sweat and moisture excellently, however they are a bit pricey.
Intent analysis
An intent analysis differs a bit from the others because it doesn’t focus on emotions but on intention. It uses advanced machine learning algorithms to identify whether a person is curious, has a complaint, or wants to make a purchase. For example a question like “Can you buy them online too?” definitely refers to a buying intent. A sentence like “I ordered it, but I haven’t received your confirmation yet.” is a complaint, while “[friend’s username], I was talking about these! You’ll love them!” is a recommendation.
What’s AI’s Role in Sentiment Analysis?
Sentiment analysis itself is a labor-demanding task. Just imagine the sheer number of long or short texts one must read and analyze. AI transformed this previously manually performed task into a rapid and more precise routine.
Quantity and speed: AI is able to process a large volume of data in a fraction of time. From social media posts and online reviews to articles, it scans through millions of texts and provides a comprehensive picture of audience sentiment.
Contextual understanding: If trained consistently, natural language processing models can recognize some level of sarcasm, idioms, and slang. This is especially important in an age when people shorten their sentences and use borrowed words and figurative language in their communication.
Continuous learning and trend forecasting: Recurrent Neural Networks (RNNs) and Long-Short Term Memory (LSTM) models are sophisticated AI systems that interpret the relationship between words and can analyze long texts such as comment threads. RNNs are capable of “remembering” past data, for example, the output of a previous text analysis, and leveraging this data in future analysis. Due to these deep learning technologies, advanced AI-powered sentiment analysis can identify changes in audience sentiments, predicting new trends.
Real-time data evaluation and scalability: Brands can monitor customers’ emotions in real time and quickly intervene if they notice changes. Businesses can evaluate a massive volume of data in mere seconds allowing them to scale their influencer partnerships and campaigns.
Multilingual proficiency: Thanks to scalability, companies can extend their campaigns to other regions. Advanced AI models help them analyze influencers’ texts in other languages.
Application of Sentiment Analysis in Influencer Marketing
In influencer marketing, sentiment analysis may be used in numerous scenarios, however, the most common use case is to understand how the audience relates to the influencer’s content - and in light of this to the brand.
Due to real-time tracking, businesses can take steps quickly if they see that people react mostly negatively or neutrally to a promotion. The marketing team can change the image or the wording of the message or they can address complaints in a jiffy. If they see the content receives overwhelmingly positive reaction, they can amplify the message with paid ads.
Sentiment analysis enables brands to see whether the influencer’s content aligns with their goals and mission. Maybe the audience doesn’t like a particular influencer or their style seems mismatched to the brand's style. Thanks to this information, businesses can optimize their campaign strategy, such as improving the content or focusing on another creator.
Negative reactions can spread like wildfire on social media, bringing down an entire company within a few weeks. Unfortunately, a controversial move from an influencer can endanger the reputation of the brand they work with. Sentiment analysis may be used for maintaining brand reputation and managing crises. A business can distance itself from the creator or release an official statement and discontinue the campaign.
An overview of audience sentiments can reveal how people feel about your brand and what they say about your products. Users’ negative feedback provides an opportunity for brands to improve their product line. Positive reviews help them highlight the most valuable features of their products in future campaigns.
Sentiment analysis is a great tool to gauge the reaction of your competitor’s audience to their campaigns and learn about customer preferences and dislikes.
AI’s Limitations in Sentiment Analysis
No matter how rapidly AI technologies develop, people are always one step ahead.
With texting and chatting people want to type in a message fast and often shorten certain words. Think of how teenagers use “sus” instead of “suspicious” or how the abbreviation “fam” supersedes “family” in many online conversations. New slang words emerge like “slay it” and replace old ones like “kill it” or “nail it”.
Languages borrow words from other languages that sometimes get distorted. Words get new meanings. Some expressions can differ based on culture or location. Langue is organic and constantly evolves. Therefore, AI tools have to be trained and retrained with updated data to avoid misinterpretations.
NLP can understand some sarcasm and cynicism but it still struggles to catch them in every situation. AI may interpret phrases like “Just what I needed” or “Wow, what an ‘excellent’ service, guys!” as positive when they originally expressed frustration or dissatisfaction.
People’s expressions are influenced by their backgrounds and experiences. They phrase complex sentences where they share - many times - opposing emotions. Someone may leave a review about an app describing how initially they disliked it and now how they changed their mind due to fixes and updates. Someone else may dislike a TV program because the storytelling is too slow-paced and grim for them, while they acknowledge that it will appeal to others.
In conclusion, AI tools can replace labor-inducing and time-consuming tasks but they need to be trained with current data to remain helpful. This is a huge undertaking for developers thanks to the evolving nature of languages and the rapid spread of informal speech used on social media.
