Sentiment analysis is the task of natural language processing where we detect a positive or negative sentiment from a piece of text. Sentiment analysis is used by companies to analyze the opinions of customers about their products or services so that they can use the positive sentiments to market their products or services and the negative sentiments to improve the quality of their products or services. If you want to know the best approaches that you can use when working on the sentiment analysis task, this article is for you. In this article, I’ll walk you through some of the best approaches for sentiment analysis.
Best Approaches for Sentiment Analysis
There are many pre-trained models, deep neural network architectures, and machine learning algorithms that you can use for sentiment analysis. Most of the time, choosing the best approach also depends on the kind of dataset you are working on, for example, whether your dataset contains labels or not. So below are some of the best approaches for sentiment analysis that you can choose according to your dataset.
VADER stands for Valence Aware Dictionary for Sentiment Reasoning. It is a lexicon and rule-based classification model for sentiment analysis, specially designed for sentiments expressed on social media platforms. It is available in the NLTK library in Python and can be used on a dataset that is not labelled. So if you are working on a sentiment analysis task where your dataset does not have sentiment labels, you can use this model. You can find a tutorial on using VADER for sentiment analysis from here.
Naïve Bayes Algorithm
If your dataset is labelled and your task is to train a classification model to classify the sentiment of a text in real-time, then you can prefer to use the Naïve Bayes algorithm. The Naïve Bayes algorithm is one of the best classification algorithms that classify data based on languages better as compared to other classification algorithms. If your data contains only two labels (for example, positive or negative), then you can use the Bernoulli Naïve Bayes algorithm, and if your dataset contains more than two labels (for example, positive, negative, neutral), then you can use the Multinomial Naïve Bayes algorithm.
The VADER sentiment model and the Naïve Bayes Algorithm are the two best approaches that I always prefer when working on the Sentiment Analysis task. If you are working on a sentiment analysis task where your dataset does not have sentiment labels, you can use the VADER sentiment model, and if your dataset is labelled and your task is to train a classification model to classify the sentiment of a text in real-time, you may prefer the Naïve Bayes algorithm. I hope you liked this article on the best approaches for sentiment analysis. Feel free to ask your valuable questions in the comments section below.