Dear all,
Dr. Joanna Michalak (Nicolaus Copernicus University in Torun, Poland) will
give a seminar entitled "Integrated Approach to Sentiment Analysis:
Optimizing Short Text Analysis Efficiency in the Context of Economics and
Finance" on November 14th at 09:30 am in Room 5 at Macro Teaching Hub
Engineering Area., Via del Politecnico, 1, University of Roma "Tor
Vergata", Roma. The seminar can also be followed online by sending an
email to roberto.monte(a)uniroma2.eu
Abstract:
Big social data (BSD) is widely used in the social sciences, particularly
in economics and finance, where it is valued both for its explanatory
insights and its predictive power. Predictive
analyses leveraging BSD often focus on modeling the relationship between
social media activity and stock market movements. The quality of these
analyses relies on two main
dimensions: (1) models that capture short-term dynamics and account for
observed time-series properties, and (2) sentiment analysis methods that
are specifically adapted to the
unique vocabulary of finance and economics.
This presentation addresses these two dimensions. In the first part, we
explore a case study of Apple Inc., comparing the effectiveness of VAR,
GARCH, and LSTM models for describing
short-term interdependencies between social media sentiment and stock
market behavior. The second part of the presentation focuses on the
performance of various sentiment
analysis methods selected to construct sentiment-based indices. This
section aims to compare the efficiency and accuracy of different sentiment
analysis methods when applied to
short text data.
The study emphasizes optimization of analysis efficiency by evaluating
widely used datasets, such as Senti140. Several research questions guide
this investigation:
1. Which sentiment analysis method is most effective in terms of accuracy
and processing speed in financial data analysis?
2. Do advanced methods, such as deep learning models (e.g., BERT),
outperform traditional lexicon-based techniques and classical machine
learning algorithms?
3. What are the differences in sentiment analysis accuracy between general
datasets like Senti140 and finance-specific datasets such as FinBERT?
The issue of optimizing text data analysis methods remains an open aspect
of this research. The ultimate objective of this study is to construct a
dataset or lexicon specifically designed
and recommended for financial data analysis.