Machine learning and Lexicon approach to Sentiment analysis

[mitsumi]

Machine learning and Lexicon approach to Sentiment analysis
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 1.53 GB
Genre: eLearning Video | Duration: 23 lectures (3 hour, 25 mins) | Language: English

 Machine learning and Lexicon based approach to Sentiment analysis using Twitter API

What you'll learn

    How to create twitter developer account and connect to twitter API
    Download Tweets, clean and store them in to Pandas DataFrame
    Learn about Tokenization, Lemmatization, Stemming and much more
    Perform Sentiment analysis with Vader and TextBlob lexicons
    Learn about Machine learning approach to Sentiment Analysis
    Build and test machine learning models

Requirements

    Basic Python knowledge (I explain each step so you can understand what I am doing)

Description

Learn how to connect and download tweets through Twitter API. From there I will show you how to clean this data and prepare them for sentiment analysis. There are two most commonly used approaches to sentiment analysis so we will look at both of them. First one is Lexicon based approach where you can use prepared lexicons to analyse data and get sentiment of given text. Second one is Machine learning approach where we train our own model on labeled data and then we show it new data and hopefully our model will show us sentiment. At the end you will be able to build your own script to analyze sentiment of hundreds or even thousands of tweets about topic you choose.

Who this course is for:

    Beginner Python developers curious about data science
    Anyone who is interested in data analysis
    People who wants to include sentiment analysis for their projects

Download link:

Só visivel para registados e com resposta ao tópico.

Only visible to registered and with a reply to the topic.

Links are Interchangeable - No Password - Single Extraction