Sentiment Analysis of Consumer Feedback and Its Impact on Business Strategies by Machine Learning

Аннотация

Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consumer sentiment, guiding decision-making and enhancing competitive advantage. The study also addresses challenges such as data ambiguity, ethical considerations, and computational The American Journal of Applied Sciences 6 https://www.theamericanjournals.com/index.php/tajas The American Journal of Applied Sciences demands, offering practical recommendations for primarily driven by improvements in computational implementing scalable and effective sentiment linguistics and artificial intelligence. Early methods analysis solutions. These results demonstrate the relied on rule-based systems and lexicons such as potential of machine learning-driven sentiment SentiWordNet, which mapped words to predefined analysis in shaping customer-focused business sentiment scores (Esuli & Sebastiani, 2006). While strategies and fostering growth in a data-driven effective for simple tasks, these methods struggled with market. the complexity of human language, including context, sarcasm, and idiomatic expressions. The introduction of machine learning brought a Introduction: Understanding consumer sentiment has paradigm shift, enabling the development of algorithms become an essential aspect of modern business that could learn from labeled datasets. Logistic strategy. In an era where consumers engage regression and support vector machines (SVM) became extensively through digital platforms, feedback popular techniques for text classification tasks, offering analysis provides businesses with actionable insights improved accuracy overrule-based systems (Pang et al., into customer preferences, complaints, and 2002). However, their reliance on manual feature expectations. This process, known as sentiment extraction limited their scalability and adaptability to analysis, leverages natural language processing (NLP) new domains. and machine learning algorithms to classify text data into positive, neutral, or negative sentiments. The Deep learning models marked the next milestone in ability to analyze consumer sentiment effectively sentiment analysis. Recurrent Neural Networks (RNNs) allows organizations to shape business strategies that and their variants, such as LSTM, demonstrated superior enhance customer experience, foster brand loyalty, performance by capturing sequential dependencies in and optimize marketing campaigns. text data (Hochreiter & Schmidhuber, 1997). These models were particularly effective for long texts, where Sentiment analysis plays a pivotal role in industries context plays a crucial role in determining sentiment. such as e-commerce, hospitality, and finance, where More recently, transformer-based models like BERT customer feedback directly impacts decision-making have set new benchmarks in sentiment analysis. By and profitability. For example, customer reviews on leveraging bidirectional context and pre-training on platforms like Amazon or Yelp often determine massive text corpora, BERT achieves state-of-the-art product sales and brand reputation. Beyond customer results in various NLP tasks, including sentiment experience management, sentiment analysis also classification (Devlin et al., 2019). influences broader business areas such as market research, competitive intelligence, and public The business applications of sentiment analysis are vast relations. With the advent of advanced machine and varied. In the retail sector, sentiment analysis helps learning models such as LSTM (Long Short-Term brands understand customer preferences and tailor Memory) networks and transformer-based their offerings accordingly (Kotler & Keller, 2016). In the architectures like BERT (Bidirectional Encoder financial industry, analyzing sentiments from news Representations from Transformers), the accuracy and articles and social media posts aids in market prediction scalability of sentiment analysis have significantly and risk assessment (Bollen et al., 2011). Furthermore, improved (Devlin et al., 2019). sentiment analysis is increasingly being integrated into customer relationship management (CRM) systems to Despite its growing importance, challenges remain in provide real-time insights and enhance customer the field, including the handling of ambiguous satisfaction (Sharma et al., 2020). language, domain-specific terminology, and cultural nuances. Additionally, ensuring data quality and Despite these advancements, challenges persist. developing r