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Sentiment Analysis with LSTM and Keras in Python
Rating: 4.3 out of 5(64 ratings)
851 students

Sentiment Analysis with LSTM and Keras in Python

Learn how to do Sentiment Classification using LSTM in Keras and Python.
Created byAbhishek Kumar
Last updated 6/2021
English

What you'll learn

  • What is Sentiment Analysis
  • What are RNN and LSTMs
  • How to apply LSTM in Keras for Sentiment Analysis

Course content

7 sections • 19 lectures • 2h 48m total length

  • Introduction2:10

    Explore the fundamentals and applications of sentiment analysis using the Get US framework and Biton, covering rule-based, automatic, and hybrid approaches, data preprocessing, model design, training, evaluation, and predictions.

  • Introduction to Sentiment Analysis2:12

Requirements

  • Basic Python programming

Description

Sentiment analysis ( or opinion mining or emotion AI) refers to the use of natural language processing(NLP), text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Simple RNNs are not good in capturing long-term dependencies. In this course we unleash the power of LSTM (Long Short Term memory) using Keras.

Who this course is for:

  • Data scientists
  • Machine Learning Engineers
  • Applied Scientists
  • Research Scientists
  • College Students

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