A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

Ranjan, Mritunjay; Tiwari, Sanjay; Sattar, Arif Md; Tatkar, Nisha S.

doi:10.3390/engproc2023059181

Cited by 2 publications

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“…M Rajan et al [10]. research has focused on sentiment analysis, which uses natural language processing (NLP) approaches to examine textual data from social media postings, comments, and reviews.…”

Section: Related Workmentioning

confidence: 99%

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

Pingfen Liu

2024

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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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“…Bayesian structural time series (BSTS) and Bayesian vector autoregression (BVAR) models have been used for a variety of economic forecasting tasks, providing reliable estimates and probabilistic forecasts. M Ranjan et al [15]. the combination of big data analytics with natural language processing (NLP) approaches has allowed researchers to extract useful insights from unstructured textual data sources such as news articles, social media, and financial reports.…”

Section: Related Workmentioning

confidence: 99%

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

Pingfen Liu

2024

jes

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This study examines the use of artificial neural network (ANN) algorithms in machine learning for recognizing fluctuation patterns and forecasting economic indicators in the field of political economy. In today's changing global economic scene, economic forecasting is critical for informing policy decisions, directing strategic planning, and managing risks. Traditional statistical approaches and time series analysis have long been used for this purpose, but the advent of ANN algorithms provides a new way to capture the intricate dynamics of economic systems. Using ANNs' intrinsic flexibility and nonlinear modelling capabilities, this study analyzes their ability to effectively forecast future swings in economic indices. This work intends to understand the strengths, limits, and practical consequences of ANN-based forecasting models by conducting a complete assessment of literature, methodological methods, and empirical studies. This study adds to the continuing discussion of economic forecasting approaches by diving into theoretical frameworks, methodological concerns, and empirical data. Finally, incorporating ANN algorithms into machine learning offers promising opportunities for improving the understanding of economic processes and supporting informed decision-making in the political economy.

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A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 2 publications

References 27 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

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

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