TEXT MINING AND SENTIMENT ANALYSIS - 149011-ENG

courses

ID:

149011-ENG

Dettaglio:

SSD: Statistics for Economics Duration: 48 CFU: 6

Located in:

BERGAMO

Url:

Course Details:

ECONOMICS AND DATA ANALYSIS - 149-270-EN/Data Science Year: 2

Course Details:

INTERNATIONAL MANAGEMENT AND MARKETING - 184-270-EN/MARKETING Year: 2

Year:

2025

Date/time interval

Secondo Semestre (16/02/2026 - 29/05/2026)

Course Objectives

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 based on unstructured data.

At the end of the course, the student knows the main theoretical foundations and practical elements to correctly conduct textual data analysis and has a solid background in textual data extraction techniques. In particular, during the course the student acquires:

familiarity with the different types of data sources, with particular reference to big data and unstructured data;
ability to extract data from different sources, such as social media or websites;
ability to convert unstructured textual data into structured numerical data;
understanding and ability to implement natural language processing techniques, such as sentiment analysis and topic modeling.

The methods will be presented using the R software.

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 Prerequisites

None

Teaching Methods

Lectures and lab sessions where students will be stimulated with active discussions and participation to create their own case study.

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

Assessment Methods

Evaluation will be based on:

• A written final exam entailing theoretical questions and exercises to be solved with the R software.

• Project on a case study, that will allow students to get a maximum of 3 extra points on the grade of the final exam based on the quality of their work.

The course offers a wide overview on text analytics and language processing techniques. Particular attention is devoted to the quality of the data sources, in a total quality perspective. 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
Working with strings: basic tools to deal with character strings (e.g. length computation, pattern recognition, regular expressions)
Natural languages: preliminary data processing (pre-processing, tokenization, stemming, lemmatization, and named entity recognition)
Text mining: introduction and different approaches; document representation; document summarization; string distances and text similarities detection.
Sentiment analysis: design and develop methods for sentiment classification and polarity detection. Dictionary approach and machine learning approach. Text visualization. The differences between sentiment analysis and emotion detection.
Topic modeling: identifying the clustering structure of a corpus of text documents and assigning documents to the identified cluster(s); Latent Dirichlet Allocation (LDA); Structural Topic Model (STM)
How to build socio-economic indicators using sentiment analysis.
Data extraction from the web: web scraping and API.
Fairness and Errors.
Quality framework for Twitter Data.
Empirical Applications.

The course illustrates several application areas of these techniques: economic, social, business decision making.