How You Can Train Your Own Sentiment Analysis Algorithm to Rate Movies
Building a sentiment analysis predictor with a movie review dataset
In the world of big data, sentiment analysis is an extremely useful natural language processing (NLP) technique. By training a suitable model, you will be able to use your model to detect what a person feels simply by inputting the words used by the person into the model.
In this article, I will be guiding you along on how you might be able to use sentiment analysis to to determine whether a person has a positive or negative reaction towards a certain movie based on the way he/she has phrased what they feel about the movie.
As you follow along, you will be able to pick up key skills relating to sentiment analysis and be able to adjust and play around with different datasets and libraries to help you create models that suit your needs.
Preparing the Data
For this exercise, a set of 50,000 “highly polar” IMDB movie reviews with a binary (‘positive’ and ‘negative’) sentiment classification is used to train the model. Firstly, download the data as a csv file (I saved my file as “IMDB Dataset.csv:).
Since the output of the algorithm is expected to be a numerical value, the…
