Develop your marketing strategy with Sentiment Analysis
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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
4.0 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
97 students enrolled
Last updated 6/2015
English
Current price: $10 Original price: $40 Discount: 75% off
1 day left at this price!
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Includes:
  • 1.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
discovering how to use the sentiment analysis of improve your business
how to use the sentiment analysis for the benchmark
normalise and prepare a text for the analysis
extract the customer comments from Disqus, Youtube, Facebook (public pages) and Amazon
extract tweets with Google sheets
learn about the most used software for the tweets analysis
learn about the tools to analyse not just tweets but all the comments
View Curriculum
Requirements
  • Students need to have online navigation and pc basic skills
  • Students should be familiar with basic marketing concepts
Description

Attention please, the course will be completely reviewed in the next weeks! If you want to buy it, please, come back or be patient! Thanks :)


In this course we will learn about the uses of the sentiment analysis. The sentiment analysis is a part of textual analysis born in the last years, through the user comments analysis, and the use of tweets for predictive analysis. In this course we will discover the uses that can be made of sentiment monitoring, and how to use it to refine our marketing strategies.

In the first section we will learn how to structure your search, the data to extract, and how to use the sentiment for benchmarking and users analysis.

In the second section we will discover how to make the text normalisation, and in the third we will learn to use some tools for automatic analysis for sentiment in tweets and text.

The fourth section is focused on some special tools for the tweets analysis. We will also discover how to use Google spreadsheets to create a tweets database.

The fifth section is focused on user feedback analysis to improve your marketing strategies. 

The course ends with a section with some mixed tools that allow, in addition to some sentiment analysis, other kind of content analysis.

Who is the target audience?
  • Students wanting to develop the skills necessary to improve a career in digital marketing,
  • Marketers, business owners, individuals
  • People having a business and wanting to achieve greater visibility and sales through SA
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Curriculum For This Course
Expand All 29 Lectures Collapse All 29 Lectures 01:30:32
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Introduction
11 Lectures 25:50

How the sentiment analysis was born: some data to start

Preview 03:47

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

Preview 06:03

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

Preview 02:14

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

Some informations about research plan
01:54

Importance of categorization in the Sentiment Analysis

How to use the Sentiment Analysis
00:46

Content extraction vs opinion extraction
00:33

Use the Sentiment Analysis for brand reputation measuring

Brand reputation
01:50

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

Sentiment Analysis and Benchmarking
00:42

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

Common scales
01:56

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

Sentiment Analysis issues
04:15

The algorithms and machine learning for Sentiment analysis

Machine learning techniques
01:50
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Steps and tools
1 Lecture 02:58

How to normalize and clean a text for the analysis

Text cleaning and normalization
02:58
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Sentiment analysis on text
3 Lectures 10:40

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

NLTK with web tools
03:24

Let's have a look in the Semantria demo

Sentiment Analysis with Semantria
01:45

Sentiment Analysis with Sentistrenght
05:31
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Sentiment analysis on tweets
3 Lectures 08:01

Tweet extraction and analysis with the site Sentiment140

Sentiment140
01:44

Tweets and trending sentiment with Wordaffect

Wordaffect
01:40

How extracting tweets with TAGS and Google Sheets

Extracting tweets with TAGS
04:37
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Sentiment analysis on users feedbacks
7 Lectures 29:53

Detect text sentiment with Text2data

Text2data
00:57

API for Sentiment Analysis
04:08

One more tool for sentiment detecting

Sentimentanalyzer
01:15

Building a database for Sentiment Analysis

How to build a database
04:39

Building a Twitter App
01:33

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

Starting with WEKA and csv files
07:29

Create an ARFF file with WEKA and apply the Sentiment Analysis

WEKA and Arff files for sentiment analysis
09:52
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Other tools
2 Lectures 09:30

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

Meaningcloud
08:46

Vivekn
00:44
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Conclusions
2 Lectures 03:40
Recap
02:49

Conclusions
00:51
About the Instructor
Valentina Porcu
4.4 Average rating
68 Reviews
3,646 Students
13 Courses
Data Scientis

I'm a computer geek, data mining and research passionate, with a Ph.D in communication and complex systems and years of experience in teaching in Universities in Italy, France and Morocco, and online, of course!

I work as consultant in the field of data mining and machine learning and I like writing about new technologies and data mining.

I spent the last 9 years working as freelance and researcher in the field of social media analysis, benchmark analysis and web scraping for database building, in particular in the field of buzz analysis and sentiment analysis for universities, startups and web agencies across UK, France, US and Italy.