Understanding Sentiment Analysis - DZone AI

Understanding Sentiment Analysis - DZone AI

A simple chatbot is not enough to enhance your clients’ experience and boost your business performance. Sentiment analysis might be your missing piece.

A simple chatbot is not enough to talk to enhance your clients’ experience and boost your business performance. When it comes to development, you realize that you require sentiment analysis to understand their feedback.

It might not seem a particularly difficult task to a human, but discerning intent behind each comment requires significant effort and specific algorithms. Our texts, blog posts, and any other form of written communication is riddled with emotion. An AI system may have little trouble understanding what a short and straightforward “I like it” means, but, in most cases, a single message contains more layers of context, subtext, irony, and other distorting factors. To classify each message correctly and respond appropriately, AI-powered communication systems rely on sentiment analysis.

While simple sentiment analysis systems can be built by freelancers or individual programmers, you’ll require the help of a seasoned vendor with substantial expertise in the niche for the development of quality AI solutions. However, before getting down to the whole process, it is essential to understand what sentiment analysis is and how it might benefit your business. 

What Is Sentiment Analysis?

Sentiment analysis combines machine learning and text analytics to determine whether the intent behind a certain piece of text is positive, negative, or neutral. Businesses can apply it to gain insights into their customers’ experience with their own and competitors’ brands. The technology also powers automated communication, allowing chatbots to handle conversations more easily and efficiently.

In more broad terms, companies frequently use sentiment analysis for wide-scope research. For instance, this tool is useful to analyze the mentions of your products or brand on social media platforms. Sentiment analysis helps monitor the company’s online image and reputation, as well as to conduct market research more comprehensively. After gaining these insights, you can feed them into another model and receive predictions on how you should adjust your activity to improve customer satisfaction and engagement.

What Types of Sentiment Analysis Are There?

Sentiment analysis is an umbrella term for the technologies that strive to identify the emotion behind a user’s message. Each company can and should tailor it to its specific needs to gain the best results. Below are three of the most popular variations of sentiment analysis techniques that are widely used and generally sufficient for the majority of tasks.

Fine-Grained Sentiment Analysis

“Positive,” “negative,” and “neutral” classifications of the visitor’s intent are called polarity categories. Having three polarity categories is sufficient to interpret the basic sentiment of each message relatively accurately, but sometimes you need to go deeper.

Fine-grained sentiment analysis expands the number of polarity categories to give you a better perspective on people’s attitudes. Usually, this type of analysis includes five categories:

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

Besides recognizing sentiment in written text, this technique is useful to process ratings that users give your company since you can adjust the model to the rating scale the company uses.

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