That not only offers recipes but also special abilities. It helps combine ingredients efficiently guides in preparation and can even perform some tasks. In this scenario the dishes are applications or software the ingredients are components or data and the special ingredient is a “language model” which understands and generates text like a human. The expected results our goal is to provide exceptional customer service in a competitive market through data collection and analysis better understanding our customers and tailoring our services to their needs. To achieve this a mechanism for accurately classifying key themes and associated feelings must be perfected.
This classification will help us understand our customers' behavior identify their main customer concerns and address them proactively. Multidimensional analysis of data breaking it down by time location and stages of the customer journey provides us with valuable insights. To do mobile app designs service this we use innovative technologies such as the openai gpt- api sandsiv+ elastic stack and the aforementioned longchain the openai api performs aspect-based sentiment analysis helping us understand the emotional context of customer comments. Sandsiv+ allows us to manage feedback in real time and map the customer journey identifying areas for improvement.
Longchain organizes and analyzes multidimensional data providing a complete view of customer experiences. Our goal is to integrate these technologies to better understand customers and deliver a superior experience. Aspect-based sentiment analysis absa doesn't just distinguish between positive negative or neutral sentiments but digs even deeper to extract sentiments about specific aspects of a product or service. Let's see how it works by combining an llm. In our case openai gpt- . -turbo. Figure – the test phrase the phrase to try is “I just arrived at the park. The hotel is good and clean. There are long lines in front of the attractions . ” here the result figure.
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