NLP Tutorial – Sentiment Analysis using Scikit Sklearn Python on IMDB Dataset
In this video, we will do sentiment analysis and prediction on the IMDB database using sklearn python package. In this sentiment analysis algorithm on the IMDB dataset, we will start with a short introduction of Pandas, Numpy. Then I will show you how to do text cleaning and preprocessing on the raw text data. Thereafter, TFIDF is performed and Support Vector Machine (SVM) is used in prediction of sentiment on IMDB reviews data.
SVM is a supervised machine learning algorithm that can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. In last, The classification report shows a representation of the main classification metrics on a per-class basis. This gives a deeper intuition of the classifier behavior over global accuracy which can mask functional weaknesses in one class of a multiclass problem.
You can improve this sentiment analysis algorithm if you use sentiment analysis using BERT or Deep Learning approach to solve your search for the best model of sentiment analysis. This video is an example of sentiment analysis on Kaggle data. You can download this dataset from my GitHub profile.
#SentimentAnalysis #Scikit_Learn #IMDB_Dataset
🔊 Watch till last for a detailed description
01:41 IMDB Dataset Download
03:57 TFIDF Vectorization
06:08 Text Cleaning Package
12:54 Linear SVM Training
14:40 Model Testing with Real-World Data
17:27 Save ML Model with Pickle
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