Pre-Lab

By the end of this learning module, students will be able to:

What is LSTM
Application of LSTM in Sentimental Analysis.

What is LSTM

Long Short-Term Memory (LSTM), is a type of recurrent neural network (RNN) architecture designed to effectively learn from sequences of data, particularly where long-range dependencies are important. Unlike regular RNNs, LSTMs can remember important information for longer periods., making it suitable for tasks like time series prediction, natural language processing, and speech recognition.

Key features of LSTMs are:

Memory Cells: These help the network keep important information over time.
Gates: LSTMs have three types of gates [input, output, and forget gates] that manage how information flows. They decide what to remember and what to forget. Architechture is shown below.

What is Sentimental Analysis

Sentiment analysis is the process of using natural language processing (NLP) to determine the emotional tone or attitude behind a piece of text. It classifies the sentiment as positive, negative, or neutral, helping to understand opinions or feelings expressed in reviews, social media posts, and other text data.

Today, we will conduct Sentiment Analysis using an LSTM model.

Sentimental Dataset Overview.

You can find the dataset link here 👉 LINK_OF_DATASET

This dataset contains 2 features [ 'Class_index and Review_text'].

Class_index is either 0 [Negative comment] or 1 [Positive comment] and it is in int form.