# happybirds

This is a repository for a university project. The task is to create a machine learning model to predict the sentiment of a tweet about airlines based on annotated data. We use the readme for brainstorming.

ideas:

FE extraction from RAW

• UPPERCASE
• emojis
• punctuation signs (!;?;...;..)
• text length
• Timestamp

CountVectorizer bigrams y trigrams

• Ignoring case
• Ignoring punctuation
• stopwrods
• Lematizción

Comments from class 01/02/2018:

• We can use the labels to obtain more suitable vocabulary, maybe neutral comments are misleading.
• We need to do crossvalidation at some point
• The order of words can change sentiment
• For next class (17/04/2018) we have to upload to kaggle results with a most suited model
• We will need to use feature selection [we will see it in next classes]
• Naive Bayes only considers if the word is in the text, it does not consider number of appearences
• At the end of the project (03/07/2018), we will need to present a news repport talking about some airline companies

Comments from class 24/05/2018:

• 15 de June is the deadline to present boths kaggles!

• Tenemos que hacer una web/blog con una explicación técnica de lo que hemos hecho --> 3 Julio. Memoria. Que sea didactico. Link al github con el codigo si es posible.

  • Datacleaning

  • exploración de datos

  • machine learning...

  • El metodo que hemos hecho para presentar el kaggle

  • Además de esto ver qué historias podemos extraer sobre las aerolineas.

• 3 Julio haremos la presentación con un ppt o similar. 2-3 min en el programa que hemos hecho + 7 min con las preguntas/conclusiones y preguntas que nos hemos hecho.

Models

• Naive bayes

• SVM-RBF

• RF

• Boosting

Visualization

• distribution

How to handle the unevenly distributed dataset? See percents:

• negative 0.629668
• neutral 0.210155
• positive 0.160178