Sentiment and Statistical Analysis of Customer Reviews for Strategic decision on Positioning and Marketing

Customer sentiment is a significant insight for any of its markets. In our used case, analysis for online shopping is done, giving far more precise marketing information using Natural Language Processing (NLP). The method extracted customers' opinions and sentiments about apparel and how they were introduced online. Data was extracted from chats, blogs, and other open sources working for the women's apparel brands. A large standardized dataset of 2.3 million responses for data mining for frequency, trend, colors, apparel type, and preferences was used. Findings from this study stated that customers prefer shopping online out of convenience and technology aids, but women's apparel has its ebbs and flaws in the case of online shopping. The study gave insights to allow marketers to resolve online shopping customer issues by analyzing their sentiments; these were precise outcomes for apparel management and decision-making. The implications were the insights where the NLP analysis was further exemplified using The Boston Consulting Group (BCG) Matrix employed to assist marketers about the weaker and stronger products online. This also aided in categorizing the merchandise under cash cow, star, question mark, and dog together, improving their shopping experience and from the marketing team's point of view to make better marketing decisions about their products. This method gives a precise categorization for differentiation and the agility needed for online marketing. We use "Machine Learning models" like "Support Vector Machine and Naïve Bayes" to calculate the accuracy, precision, and recall. Precise results were obtained by exploring these models and algorithms in Python.