4 ways to develop a sentiment analyzer in machine learning 61af81ca37811 1

Discover 4 ways to develop a sentiment analyzer in Machine Learning.

With the help of machine learning (ML), technology continues to grow, evolve, and get modernized. Not only does it help transform the industry through automation, but its algorithm can also sort out data, which can help identify patterns in detecting emotions and sentiments in a given raw dataset. That’s where sentiment analysis comes in.

What is Sentiment Analysis?

Sentiment Analysis is a type of language process that has the ability to identify emotions, moods, and feelings in a certain text. A text can either be interpreted as positive, negative, or neutral.

It’s commonly utilized by businesses to help them understand how their products, services, even their company’s brand are being perceived and regarded by their consumers. Through this information, businesses can be able to revisit and improve certain aspects of their services, to best cater to the needs and preferences of their consumers. For instance, online newspapers and media have turned to sentiment analysis apps, to make sense of what their audiences are thinking at a given moment.

An ML algorithm model can identify and learn various sentiments by studying and mining data patterns. You can use python analysis to do this.

With that said, here are some ways to introduce and develop a sentiment analyzer in machine learning.

1. Custom-Trained Supervised Learning

One of the ways to develop a sentiment analyzer is by training a custom machine learning or deep learning model. In this approach, you’ll need to have a labeled dataset or training dataset. The ML algorithm model will look into the labeled dataset, with the goal of identifying patterns and the correlations between those patterns and certain sentiments and then learn from them.

With that said, when they’re fed with new data, the ML model then applies whatever it learned from the labeled dataset when analyzing and processing the newly given information. If it sees something in the new data which looks similar to what it saw in the labeled data, it will then make a prediction.

Here are the usual steps in training a custom sentiment analysis model:

  • Collect raw data to be used as your labeled dataset
  • You’ll have to preprocess the text to be used for training
  • Text will then be encoded numerically
  • The appropriate ML algorithm model has to be selected
  • The ML algorithm model will then undergo training and hyper tuning
  • The ML algorithm model will be tasked to make prediction

2. TextBlob

Python, as a programming language, comes with a lot of libraries that are used to aid in coding and programming tasks. TextBlob is one of its open-sourced libraries, that can be used to process data in text form.

The application programming interface (API) of TextBlob contains algorithms, commands, and functionalities, which allow the user to process the textual data. The API can be used to classify parts of the text, extract noun phrases, and translate words, sentences, and paragraphs. It can also be used to perform tasks such as tagging parts-of-speech and sentiment analysis- through its polarity and subjectivity values, and many other tasks.

3. Word-Dictionary Based Model

One of the methods to develop a sentiment analyzer is by creating what is called an N-gram dictionary. An N-gram dictionary is composed of positive and negative words which are based on a text corpus. To create this dictionary, custom python functions would be used to train and develop the ML algorithm model.

This model has the added advantage of being able to add custom words to the dictionary, derived from domain knowledge.

4. BERT

Another method of developing a sentiment analyzer is the Bidirectional Encoder Representations from Transformers (BERT). Since its launch, BERT has been regarded as a modern machine learning model. It was designed and developed to be used for tasks in natural language processing (NLP). It has two components- an encoder and a decoder. The encoder reads the given text, while the decoder puts together a prediction. It was designed to help computer systems understand relations between words and sub words in a given text.

Conclusion

ML algorithm models have evolved to become a powerful tool in analyzing almost everything, even those things which were previously thought of as beyond the realm of artificial intelligence and automation. Introducing sentiment analysis is one of the various ways to boost artificial intelligence. They can be significantly useful in analyzing the emotions, feelings, or mood of a certain community, group, audience, in a given corpus of text.

With the help of sentiment analysis, data analysts of businesses and companies can get information about how they are being perceived by their consumers. Consider these ways and use modern technology to your business’ advantage.

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