Sentiment Analysis in data science often called Opinion Mining or Emotion AI is used to identify, extract, quantify, and study the text and understand the classification of the text as positive or negative, or neutral.
Sentiment analysis is the mining of words that identify a brand’s social feelings and also helps the company determine whether the product it produces creates market demand or not. The goal of Sentiment analysis is to analyze people’s opinions in a way that helps expand the business.
It focuses not only on the tone (positive, negative and neutral) but also on emotions (happy, angry, sad, funny etc.). This analysis uses various NLP (natural language processing) algorithms such as rule-based, hybrid, and automatic.
How Sentiment Analysis Work
The most straightforward implementation of this analysis is using a scored word list. You can break a piece of text into individual words and compare them with the text list to decide the final sentiment score. For example, AFINN is a list of textual words scored with numbers between -5 to +5.
Suppose that we had the phrase, “I love dogs, but I am allergic to them”.
In the AFINN text list, you can find two words, “love” and “allergic”, with their respective scores of +3 and -2. By combining these two scores, you get a total score of +1, so you can classify this sentence line as mildly positive. You can ignore the rest of the text in the sentence.
There are more complex uses of sentiment analysis in the industry today. These algorithms can provide you with accurate scores for long pieces of text. Besides that, the techies are working on the technology to make it better over time.
For complicated models, you can use a combination of NLP and machine learning algorithms. There are three major types of algorithms used in sentiment analysis. Here are those types:
- Automated Systems
- Rule-based Systems
- Hybrid Systems
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