Introduction
Sentiment analysis is a technique through which you can analyze a piece of text to determine the sentiment behind it. It combines machine learning and natural language processing (NLP) to achieve this. Using basic Sentiment analysis, a program can understand if the sentiment behind a piece of text is positive, negative, or neutral.
It is a powerful technique in Artificial intelligence that has important business applications. For example, you can use Sentiment analysis to analyze customer feedback. You can collect customer feedback through various mediums twitter, Facebook, etc. and run sentiment analysis algorithms on them to understand your customer ‘s attitude towards your product.
How Sentiment Analysis Works
The simplest implementation of sentiment analysis is using a scored word list. For example, AFINN is a list of words scored with numbers between minus five and plus five. You can split a piece of text into individual words and compare them with the word list to come up with the final sentiment score.
eg. I love cats, but I am allergic to them.
In the AFINN word list, you can find two words, “love” and “cats” with their respective scores of +2 and -3. You can ignore the rest of the words (again, this is very basic sentiment analysis). Combining these two, you get a total score of +1. So you can classify this sentence as mildly positive.
There are complex implementations of Sentiment analysis used in the industry today. Those algorithms can provide you with accurate scores for long pieces of text. Besides that, we have reinforcement learning models that keep getting better over time.
For complex models, you can use a combination of NLP and machine learning algorithms. There are three major types of algorithms used in sentiment analysis. Let’s take a look at them.
Automated Systems
Automatic approaches to sentiment analysis rely on machine learning models like clustering. Long pieces of text are fed into the classifier, and it returns the results as negative, neutral, or positive. Automatic systems are composed of two basic processes:
Rule-based Systems
Unlike automated models, rule-based approaches are dependent on custom rules to classify data. Popular techniques include tokenization, parsing, stemming, and a few others. You can consider the example we look at earlier as a rule-based approach.
A good thing about rule-based systems is the ability to customize. These algorithms can be tailor-made based on context by developing smarter rules. However, you will have to regularly maintain these types of rule-based models to ensure consistent and improved results.
Hybrid Systems
Hybrid techniques are the most modern, efficient, and widely-used approach for sentiment analysis. Well-designed hybrid systems can provide the benefits of both automatic and rule-based systems.
Hybrid models enjoy the power of machine learning along with the flexibility of customization. An example of a hybrid model would be a self-updating wordlist based on Word2Vec. You can track these wordlists and update them based on your business needs.
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