The objective of this project is to build a text classification model that analyses the customer's sentiments based on their reviews in the IMDB database. The model uses a complex deep learning model to build an embedding layer followed by a classification algorithm to analyse the sentiment of the customers
DATA DESCRIPTION:
The Dataset of 50,000 movie reviews from IMDB, labelled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, the words are indexed by their frequency in the dataset, meaning the for that has index 1 is the most frequent word. Use the first 20 words from each review to speed up training, using a max vocabulary size of 10,000. As a convention, "0" does not stand for a specific word, but instead is used to encode any unknown word
PROJECT OBJECTIVE:
Build a sequential NLP classifier which can use input text parameters to determine the customer sentiments.
Steps and tasks:
Import and analyse the data set. Hint: - Use `imdb.load_data()` method - Get train and test set - Take 10000 most frequent words
Perform relevant sequence adding on the data
Perform following data analysis: • Print shape of features and labels • Print value of any one feature and it's label
Decode the feature value to get original sentence
Design, train, tune and test a sequential model Hint: The aim here Is to import the text, process it such a way that it can be taken as an inout to the ML/NN classifiers. Be analytical and experimental here in trying new approaches to design the best model.
Use the designed model to print the prediction on any one sample