Text Analytics A.Y. 2017/18

Teachers

Schedule
Day	Hour	Room
Monday	11-13	N1, Polo Fibonacci
Tuesday	9-11	N1, Polo Fibonacci

Forum

Forum on Piazza

Homeworks

Homework 2: deadline 6/11/2017.

Projects

List of Suggested Projects - Students can also propose their own project ideas.

Send proposals and preferences to attardi@di.unipi.it and andrea.esuli@isti.cnr.it

Post your questions about the project and exams on Piazza.

Once submit your work the date of the discussion will be set by appointment.

Objectives

The course targets text analytics systems and applications to respond to business problems by discovering and presenting knowledge that is otherwise locked in textual form. The objective is to learn to recognize situations in which text analytics techniques can solve information processing needs, to identify the analytic task/process that best models the business problem, to select the most appropriate resources methods and tools, to collect text data and apply such methods to them. Several applications context will be presented: information extraction, sentiment analysis (what is the nature of commentary on an issue), spam and fake posts detection, quantification problems, summarization, etc.

Disciplinary background: Natural Language Processing, Information Retrieval and Machine Learning
Mathematical background: Probability, Statistics and Algebra
Linguistic essentials: words, lemmas, morphology, PoS, syntax
Basic text processing: regular expression, tokenisation
Data gathering: twitter API, scraping
Basic modelling: collocations, language models
Introduction to Machine Learning: theory and practical tips
Libraries and tools: NLTK, Keras
Applications:
- Classification/Clustering
- Sentiment Analysis/Opinion Mining
- Information Extraction/Relation Extraction
- Entity Linking
- Spam Detection: mail spam & phishing, blog spam, review spam

Jupyter Notebook Server

A server has been setup for running Jupyter Notebooks. In order to log into the server, you must get credentials for a Google Suite account:go to this page and register with your University credentials to activate your free account.

Lecture Notes

Date	Lecture	Notes
18/9/2017	Introduction	Introduction
19/9/2017	L'età della parola	L'età della parola
25/9/2017	Introduction to Probability	Probability
26/9/2017	Language Modeling	Language Modeling
2/10/2017	Introduction to Python 1/2	Python 1/2 (notebook)
3/10/2017	Introduction to Python 2/2	Python 2/2 (notebook, homework 1)
9/10/2017	Introduction to NLTK	Introduction to NLTK
10/10/2017	Basic Text Processing	Tokenization
16/10/2017	Word Similarity	Word Similarity
17/10/2017	Text Classification	Text Classification
23/10/2017	Hidden Markov Models	HMM
24/11/2017	Named Entity Recognition	NER.pptx
6/11/2017	Classifiers	Classifiers
7/11/2017	Deep Learning for NLP	Deep Learning
13/11/2017	Neural Language Models	NLM (notebooks)
14/11/2017	Correction of Homework 2, Keras	Deep Leaning Libraries
21/11/2017	Introduction to Sentiment Analysis	Sentiment Analysis
27/11/2017	Lexical resources for sentiment analysis	Lexical resources
28/11/2017	Sentiment classification	sentiment classification (notebooks)
4/12/2017	Data collection and experiments	Data collection and experiments
5/12/2017	Spam, Scam, Phishing, Fake reviews, Clickbaits, Fake News	Spam & co
12/12/2017	Quantification (Fabrizio Sebastiani)	Quantification

Textbooks

D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.