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.
How companies are used to use Sentiment analysis, the most important sources in text mining and the languages most used for research
For the Sentiment analysis, we can use automatic or manual processing. Let's see why
Sometimes is not enough to discover the text polarity, but the Sentiment analysis can be used for specific aims
Importance of categorization in the Sentiment Analysis
Use the Sentiment Analysis for brand reputation measuring
The sentiment analysis can also be used for benchmarking analysis between the same category products
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
Let's see what are the limits and the main problems of Sentiment analysis
The algorithms and machine learning for Sentiment analysis
How to normalize and clean a text for the analysis
How to use the site text-processing for stemming and measure the text sentiment
Let's have a look in the Semantria demo
Tweet extraction and analysis with the site Sentiment140
Tweets and trending sentiment with Wordaffect
How extracting tweets with TAGS and Google Sheets
Detect text sentiment with Text2data
One more tool for sentiment detecting
Building a database for Sentiment Analysis
Let's start with Weka for Sentiment Analysis and csv files
Create an ARFF file with WEKA and apply the Sentiment Analysis
Meaningcloud categorizes and analyzes texts from the console or through the Excel extension for Windows
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.