Description
Analyzing Customer Sentiment Using Text Analytics: A Mixed-Methods Study of Social Media Data
Abstract
The study investigates data from social media utilizing a mixed-methods analysis. It utilizes text analytics and quantitative analysis of heavy volumes of social media information to examine consumer sentiment. However it would provide a fuller picture of how consumers feel about a business, product, or service. In this study, the primary components are data gathering, quantitative analysis, and qualitative analysis. Moreover based on the target population and the regions where customer conversations are most active. Select relevant social media platforms which are Instagram, Twitter, Facebook, and Reddit.
Sentiment analysis algorithms are employed in the quantitative analysis to evaluate every paragraph as either negative, positive, or neutral established on the sentiment characterized. Using known dictionaries of positive and negative phrases to gauge sentiment. To train sophisticated models (like Naive Bayes and Support Vector Machines) to identify sentiment and use context and complex linguistic patterns.In qualitative analysis, a selection of social media posts is carefully examined to identify significant themes, problems, and positive aspects that users have brought up.
Analyzing the language used, including expressions, analogies, and emotional indicators, to discern the underlying emotions beyond simple classification as good or negative. Combining quantitative and qualitative data allows researchers to see customer sentiment holistically, considering both the general trend and the subtle causes of the situation. Generally a mixed-methods study could produce useful information that companies could use to solve certain client issues and enhance their offerings in terms of goods, services, and customer satisfaction.
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Sentiment Analysis Using Text Mining: A Review
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