Sentiment Analysis

Sentiment Analysis is a natural language processing method used to determine whether data is positive, negative, or neutral. Sentiment analysis is often done on textual data to help businesses measure customers’ feelings about the brand and understand customer needs.

The general benefits of emotion analysis are:

Sorting data in large volumes

Can you manually organize thousands of tweets, customer support conversations, or polls? A business’s data is too much for manual processing. Sentiment analysis helps businesses process large amounts of data in an efficient and cost-effective way.

on time analysis

Sentiment analysis can identify important issues at the right time, for example it is very important that your public relations (PR) responds properly when a problem arises on social media.

Integrated criteria for categorizing emotions

Labeling sentiments in a text is very subjective and is influenced by personal experiences, thoughts and beliefs. It is estimated that people agree with each other only about 60-65% of the time when determining the sentiments of a particular text.

There are several algorithms that you can implement in sentiment analysis models. Below we will review some of these in more detail:

  • Rule-based: These systems automatically analyze sentiments based on a set of manually written
    rules.

  • Automatic: Systems rely on machine learning techniques to learn from data.

  • Hybrid systems: Combines both law-based and automated approaches.

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