Sequential NLP classifier I Machine learning assignment help

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:

1. Import and analyse the data set. Hint: - Use `imdb.load_data()` method - Get train and test set - Take 10000 most frequent words

2. Perform relevant sequence adding on the data

3. Perform following data analysis: • Print shape of features and labels • Print value of any one feature and it's label

4. Decode the feature value to get original sentence

5. 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.

6. Use the designed model to print the prediction on any one sample

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