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Develop your marketing strategy with Sentiment Analysis

Section 1: Introduction Lecture 1 03:47

How the sentiment analysis was born: some data to start

Lecture 2 06:03

How companies are used to use Sentiment analysis, the most important sources in text mining and the languages most used for research

Lecture 3 02:14

For the Sentiment analysis, we can use automatic or manual processing. Let's see why

Lecture 4 01:54

Sometimes is not enough to discover the text polarity, but the Sentiment analysis can be used for specific aims

Lecture 5 00:46

Importance of categorization in the Sentiment Analysis

Lecture 6

Content extraction vs opinion extraction

00:33 Lecture 7 01:50

Use the Sentiment Analysis for brand reputation measuring

Lecture 8 00:42

The sentiment analysis can also be used for benchmarking analysis between the same category products

Lecture 9 01:56

There are various scales to measure sentiment, some are based on the simple distinction between positive and negative comments, some based on 5 or 7 point scale

Lecture 10 04:15

Let's see what are the limits and the main problems of Sentiment analysis

Lecture 11 01:50

The algorithms and machine learning for Sentiment analysis

Section 2: Steps and tools Lecture 12 02:58

How to normalize and clean a text for the analysis

Section 3: Sentiment analysis on text Lecture 13 03:24

How to use the site text-processing for stemming and measure the text sentiment

Lecture 14 01:45

Let's have a look in the Semantria demo

Lecture 15

Sentiment Analysis with Sentistrenght

05:31Section 4: Sentiment analysis on tweets Lecture 16 01:44

Tweet extraction and analysis with the site Sentiment140

Lecture 17 01:40

Tweets and trending sentiment with Wordaffect

Lecture 18 04:37

How extracting tweets with S and Google Sheets

Section 5: Sentiment analysis on users feedbacks Lecture 19 00:57

Detect text sentiment with Text2data

Lecture 20

API for Sentiment Analysis

04:08 Lecture 21 01:15

One more tool for sentiment detecting

Lecture 22 04:39

Building a database for Sentiment Analysis

Lecture 23

Setting the software for comment extraction

06:22 Lecture 24 01:33 Lecture 25 07:29

Let's start with Weka for Sentiment Analysis and csv files

Lecture 26 09:52

Create an ARFF file with WEKA and apply the Sentiment Analysis

Section 6: Other tools Lecture 27 08:46

Meaningcloud categorizes and analyzes texts from the console or through the Excel extension for Windows

Lecture 28 00:44 Lecture 29 02:58

Metamind, born from Etcml, includes a lot of tools for extracting sentiment from tweets, and has some interesting demos for image categorization

Section 7: Conclusions Lecture 30 02:49 Lecture 31 00:51 Full curriculum




Develop your marketing strategy with Sentiment Analysis
A guide to discover the basics of the Sentiment Analysis, how to use it to improve your marketing and customer relations * Lectures 31 In this course we will learn

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