Dr. Samira Abasi, Dr. Fatemeh Amiri,
Volume 20, Issue 1 (6-2023)
Abstract
In the Corona crisis, we face a wide range of thoughts, feelings, attitudes, and behaviors on social media. This data contains valuable information for responding to the crisis by the people and administrators. The goal of this study is to identify the characteristics of messages that lead to different emotional polarities. This study aims to investigate the information posted by Twitter, Instagram, and Telegram users and news related to the COVID-19 pandemic in Iran. The data extracted from social networks are focused on the period of January 21, to April 29, 2020, which were shared in Iran and in Persian. It should be noted that the data set and their labels were published by the Cognitive Sciences and Technologies Council (CSTC) in Iran. In this work, the content of each post was pre-processed. Pre-processing was performed by removing stop words, normalizing the words, tokenizing, and stemming. The emotion labels were based on plutchik’s model and included joy, trust, fear, surprise, sadness, anticipation, anger, disgust, stress, and other emotions. In this study, clustering algorithms were used to analyze social media posts. We applied a two-stage clustering method. The proposed clustering algorithm was a combination of self-organized neural network and K-means algorithms. According to our proposed algorithm, the data were clustered through SOM at first, the results of which provided the initial cluster centers for the K-means algorithm. Implementations were built in Python version 3.7 and MATLAB R2015a. Hazm Tools was used for pre-processing data, and clustering was done in MATLAB. The Davies-Bouldin clustering evaluation was applied to find the optimal number of clusters. This measure was calculated for the number of clusters in the range of 2-50 in the two-stage clustering method. The results showed that the optimal number of clusters was ten. Analysis of the results showed that posts related to health and culture with negative polarity led to negative emotions such as fear, hatred, sadness, and anger. Messages about people's emotional and improper functioning have led to feelings of sadness, fear, and stress, and reduced hope in society. The results revealed a strong correlation between anger and disgust. Also, a positive correlation between fear, stress, and sadness was observed. In order to reduce the negative feelings and to create a sense of trust in the authorities, we suggest clarifying about the corona pandemic
Dr Saeede Anbaee Farimani, Dr Raheleh Ghouchannezhad Noor Nia, Dr Majid Vafaei Jahan,
Volume 22, Issue 2 (9-2025)
Abstract
The onset of social media venues, online news media, and digital content allowed a vast volume of text and time series data to be generated which plays significant role in investors' decision-making and financial market volatility. Data extracted from these platforms provide information on public sentiments, immediate reactions to news, and informal analyses, which, if processed appropriately, can be very useful indicators in forecasting financial market trends. Billions of dollars are invested and lost, depending on correct forecasting. However, advances in deep learning, especially in large language models (LLMs) and novel time series analysis algorithms, have opened new windows to processing and analyzing this complex data. The advanced language models identify hidden patterns and nonlinear dependencies, always taking into account the context and semantic details of the text between news, market sentiments, and price fluctuations, as well as utilizing them via intelligent market analysis systems. This review analyzes the existing research trends on the relationship of text data available on websites and social networks with the behavior of financial markets, having reviewed more than 200 scientific papers published between 2006 and 2024 in a systematic manner. This study focuses on identifying advanced methods within text representation, sentiment analysis, predictive modeling, and language model applications for analyzing real-time and unstructured data. More than one information source has to be taken into consideration: (Twitter, news agencies, blogs, and specialized forums) from a perspective of credibility, data structure, and influence-on market decisions. Given the complexity of financial markets, such as stocks and forex, there is an ever-increasing demand for hybrid models capable of carrying out analyses across time-series and text data simultaneously. This paper aims to analyze the current research accomplishments, identify gaps in the research, and ultimately put forward future directions for the fields of text mining, AI, and deep learning. These directions can open up the path for the next generation of real-time and adaptive recommender, predictor, and correlation analyzer systems in the financial markets.
