TEXT MINING AND SENTIMENT ANALYSIS | Università degli studi di Bergamo

TEXT MINING AND SENTIMENT ANALYSIS

Attività formativa monodisciplinare
Codice dell'attività formativa: 
149011-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2020/2021
Insegnamento (nome in italiano): 
TEXT MINING AND SENTIMENT ANALYSIS
Insegnamento (nome in inglese): 
TEXT MINING AND SENTIMENT ANALYSIS
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
STATISTICA ECONOMICA (SECS-S/03)
Anno di corso: 
1
Anno accademico di offerta: 
2020/2021
Crediti: 
6
Responsabile della didattica: 
Altri docenti: 
Mutuazioni

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Lingua: 
Inglese
Ciclo: 
Secondo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
48
Ore di studio individuale: 
102
Ambito: 
Statistico-matematico
Prerequisites

None

Educational goals

The course "Text mining and sentiment analysis", consistently with the skills that the course of study intends to achieve, provides students with knowledge related to the use of quantitative and economic statistic tools necessary to carry out a rigorous empirical analysis. The focus of the course is on unstructured data. Students will acquire a solid background on text analytics techniques from the theoretical and practical perspective. They will also become familiar with the different types of data sources, with particular reference to unstructured and big data.
At the end of the course, students will be able to process the unstructured information contained in text data in order to make text as informative as standard structured data and allow to investigate relationships and patterns which would otherwise be extremely difficult, if not impossible, to discover. Further, students will be able to categorize and cluster text to provide economic statistic information and to devote attention to the quality of the data sources, in a total quality perspective.
They will be able to address specific problems in the area of text mining and sentiment analysis. In particular, they will know the main notions needed to understand text processing, foundations of natural language processing, text classification, and topic modeling. Moreover, students will be able to deal with sentiment analysis in the context of opinion mining and rule-based models and alternative learning models for text.

The course has the following specific objectives:
- Students will understand what text analytics is and will learn natural language processing techniques, such as sentiment analysis.
- They will learn how to convert unstructured text-based character data into structured numeric data.
- They will become aware of the pros and cons in the use of unstructured data, also in terms of quality.

The organization of the course and assignments will allow students to develop communication skills and the ability to work both in groups and independently and to effectively present the results of their research work and deliver it in the required time.
Ability to process and analyze unstructured data is learned using one of the most widely spread statistical software: R.
The course is fully coherent with the education aims of the EMOS (European Master in Official Statistics) label as well as for the Master course in Economic and Data Analysis.

Course content

The course offers a wide overview on text analytics and language processing techniques. Particular attention is devoted to the quality of the data sources. An example of framework criteria for social data quality is introduced.
Topics covered:
• Unstructured data and Big data: what they are, how to use them; characteristics of different data sources; Big data and unstructured data as a source for economic analysis in a context of integrated data sources is introduced.
• Natural languages: classification techniques and preliminary data processing (tokenziation, part-of-speech tagging, chunking, syntax parsing, and named entity recognition)
• Text mining: introduction and different approaches; document representation, text categorization and clustering (identifying the clustering structure of a corpus of text documents and assigning documents to the identified cluster(s); typical types of clustering algorithms, i.e., connectivity-based clustering (hierarchical clustering) and centroid-based clustering (e.g., k-means clustering)),document summarization.Sentiment analysis: design and develop methods for text classification and topic modeling. Design and develop methods for sentiment classification and polarity detection. Dictionary approach. Text visualization. The differences between sentiment analysis and emotion detection.
• How to build socio-economic indicators using sentiment analysis.
• Fairness and Errors.
• Quality framework for Twitter Data.
• Applications. The course illustrates several application areas of these techniques: economic, social, business decision making.
• Lab sessions for applications using statistical software: R.

Teaching methods

Lectures and lab sessions (students will be stimulated with active discussions and participation to create its own case study).

Individual cases or personal projects developed by students according to the themes proposed by the teachers.

Assessment and Evaluation

Evaluation can be based on:
• A theoretical oral final exam.
• Oral discussion about case studies, research results and/or based on a deeper discussion of the course topics.
• Assessments provided by the professors, including case studies, reports and ppt presentations can be proposed and will be considered as part of the final evaluation

Further information

Since Eurostat is providing every year new innovative teaching material for EMOS labeled masters, in order to obtain high quality and innovative educational standards (recognized at international levels), the teaching activity will be constantly updated.

Important note: if the course will take place (partially or fully) online, some changes can be introduced in the course syllabus. This in order to adapt both the course and the exam to an online attendance.