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ABSTRACT
Introduction: In the digital age, online product reviews play a pivotal role in consumers’ decision-making processes. Leveraging sentiment analysis, businesses can gain valuable insights from these reviews to improve their products and services.
Understanding Sentiment Analysis: Sentiment analysis, also known as opinion mining, involves analyzing textual data to determine the sentiment expressed towards a particular product or service. It utilizes natural language processing techniques to classify opinions as positive, negative, or neutral.
Data Collection and Preprocessing: The first step in online product review analysis is collecting data from various sources such as e-commerce websites and social media platforms. Once collected, the data undergo preprocessing, including text normalization, tokenization, and removal of stopwords to prepare it for analysis.
Evaluation Metrics: To assess the performance of the sentiment analysis model, various evaluation metrics such as accuracy, precision, recall, and F1-score are utilized. These metrics measure the model’s ability to correctly classify sentiments and its overall effectiveness in analyzing online product reviews.
Application in Business Decision-Making: Businesses can leverage the insights obtained from sentiment analysis to make informed decisions regarding product development, marketing strategies, and customer satisfaction initiatives.
Real-World Impact: By incorporating sentiment analysis into their operations, companies can enhance customer experiences, build brand loyalty, and stay ahead of market trends. Additionally, consumers benefit from more transparent and reliable information, enabling them to make better purchasing decisions.
Conclusion: Online product review analysis using sentiment analysis provides businesses with actionable insights derived from consumer feedback.
