A Comprehensive Overview of Text Mining Techniques for Price Prediction

Price prediction plays a crucial role in various domains, including finance, marketing, and economics. In recent years, text mining techniques have emerged as powerful tools for extracting valuable insights from textual data and enhancing the accuracy of price-prediction models. This paper presents a comprehensive overview of text mining techniques employed for price prediction across different domains. Through a systematic review of ۳۶ relevant literature from ۲۰۱۳ to ۲۰۲۳, this paper examines the application of text mining techniques, such as sentiment analysis, topic modeling, and natural language processing, in price prediction research. The review reveals that text mining techniques have been successfully utilized in financial markets, including stock markets, and foreign exchange markets, where textual data from sources like news articles, social media, and financial reports are leveraged to uncover hidden patterns and sentiments that impact price movements. Moreover, the application of text mining techniques in non-financial domains such as energy markets, healthcare, and consumer goods is also explored. The findings demonstrate that text mining techniques provide valuable insights for price prediction, enabling traders, investors, and decision-makers to make more informed and data-driven decisions. The paper emphasizes the importance of text mining techniques in price prediction research and identifies potential avenues for future research in this field.

کلیدواژه ها:

Text mining ، Financial markets ، Pricing decisions ، Price forecasting ، online reviews.