Today, customers and consumers have social media and various platforms for reviews the opportunity to use their Opinion and reviews about all possible products and services. Since you usually look at the reviews before you make a purchase, the opinions of other users are very important. Companies are therefore also very interested in being informed about what their customers think of the products or services. In order to be able to react to customer feedback and reviews as well as trends more quickly, AI chatbots are being used more and more frequently. The analysis of the mood in comments on various platforms is called Sentiment Analysis.
In brief in advance:
- Sentiment analysis is used to obtain a picture of customer sentiment
- Customers' opinions are relevant for companies to be able to adapt their business model
- Language models are used for the analysis, which model text and speech in order to be able to process them
- Automatic processing through AI should make categorization faster and more reliable
- The tools used can be based on rule-based or hybrid approaches as well as on machine learning or deep learning
What is sentiment analysis?
At the Sentiment analysis, also known as 'opinion mining, is about recording customer communication and Opinions, sentiment, and emotions in comments from users on social media to extract. The automatic analysis should be used in the Collecting large amounts of data help improve products, services, or customer service. This is how well-founded and strategic decisions are made. For this purpose, language models are used to emotions to recognize and understand what users really meant. This page gives an overview about the opportunities of sentiment analysis.
What language models are there?
- Rule-based approaches
- Machine learning (e.g. deep learning)
- Hybrid approaches: rule-based and automated
Some Tools are based on the rule-based approach, which also includes the creation of a dictionary. In doing so, glossaries with Keywords created, which should be recognized in user texts and rated as positive or negative. So-called stop words such as pronouns or articles are excluded because their meaning is not relevant to sentiment analysis. They are therefore trained manually or semi-automatically, using the Keywords-in-context function (KWIC) Use back because, for the corresponding keywords, the Context is crucial for correct interpretation is. Similar to other language models such as ChatGPT, the context is statistically evaluated — this allows them to learn speech recognition and output in the first place. The tokenization and annotation of voice data also plays a central role here.
Taking context into account is also important for AI-powered models central to correctly define a keyword as Classify positively or negatively to be able to. Approaches such as Deep learning Belongs to Machine Learning (ML), where Algorithms themselves recognize patterns in texts such as comments, posts, reviews, or emails. Deep learning uses artificial neural networks of input and output layers. These layers consist of nodes, which are given numbers, which are then linked to form different layers. It combines various algorithms.
This modelling is important because AI cannot understand language. A reliable machine learning algorithm for sentiment analysis is also Support Vector Machine. With regard to a characteristic, similarities between words are emphasized in order to make them classify. They are given values from -1 to 1 to create vectors. Similar values therefore result in a similar semantic classification. Classification can also be carried out using the linear regression take place. It is also part of ML and comes from statistics. It describes a value (y) based on properties (x).
These models capture the text and its general, conveyed feeling. This polarity may be positive, negative, or neutral. Texts are therefore referred to as categorized positively or negatively. With IBM you will receive comprehensive information about the various models.
Why is an AI trained for this?
Humans can recognize emotions better than machines. What AI can do better than humans is Quick capture of large amounts of text, because people have to read them step by step and get tired in the process, if you like. Companies expect to identify trends and customer dissatisfaction at an early stage in order to Adapt your business model as quickly as possible. The competitive pressure makes it necessary for companies in general to make use of it. As a result, such AI tools are getting hyped and fast overrated become. It remains important to control the AI that you use to ensure that it delivers the right results.
