OFML-SMF: Optimal feature selection with hybrid machine learning classifier for stock market price forecasting using social media and secondary data sources

Rao, K. Venkateswara; Reddy, Bale V.

doi:10.1007/s11042-023-15461-3

Cited by 3 publications

(1 citation statement)

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“…This extensive data source helps organizations to track brand sentiment, evaluate product strengths and shortcomings, and modify marketing campaigns to better suit consumer needs. KV Rao and BVR Reddy [16]. research investigations have investigated the impact of social network data analysis on financial markets and investing decisions.…”

Section: Related Workmentioning

confidence: 99%

Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis

Pingfen Liu

2024

jes

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It examines the junction of consumer behavior prediction and market application exploration using social network data analysis. Using the massive quantity of information available on social media sites, they use complex data mining tools to estimate consumer opinions, preferences, and behaviours. They discover useful insights that inform strategic decision-making processes for enterprises across several industry domains by combining approaches such as predictive modelling, natural language processing, and social network analysis. Results show that social network data analysis can effectively anticipate consumer actions and detect market trends. They employ sentiment analysis to categorize user sentiments regarding products, companies, and marketing campaigns, delivering actionable insights for marketing plan optimization. Furthermore, predictive modelling helps us to estimate purchase intent, segment clients, and spot new trends, allowing businesses to customize their products and improve customer engagement. Additionally, this study emphasizes the significance of addressing ethical concerns and privacy consequences in social network data analysis. Businesses that embrace transparent and responsible data policies can create consumer trust while also mitigating the dangers related to data misuse.

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Section: Related Workmentioning

confidence: 99%

Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis

Pingfen Liu

2024

jes

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Predictive Modeling of Stock Prices Using Machine Learning: A Comparative Analysis of LSTM, GRU, CNN, and RNN Models

Dželihodžić,

Žunić,

Žunić Dželihodžić

2024

Lecture Notes in Networks and Systems

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Remote Sensing Estimation Method of Urbanization Level Based on Multi-Source Data Analysis

Li,

2024

Sustainable Civil Infrastructures

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OFML-SMF: Optimal feature selection with hybrid machine learning classifier for stock market price forecasting using social media and secondary data sources

Cited by 3 publications

References 32 publications

Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis

Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis

Predictive Modeling of Stock Prices Using Machine Learning: A Comparative Analysis of LSTM, GRU, CNN, and RNN Models

Remote Sensing Estimation Method of Urbanization Level Based on Multi-Source Data Analysis

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