Predicting Consumer Sentiment Through Natural Language Processing for Strategic Marketing Decisions

Consumer sentiment expressed in reviews, social posts and support interactions is a leading indicator of demand, yet marketing decisions are usually made on lagging sales data. This paper examines how natural language processing (NLP) converts text into a sentiment index that leads sales, how much the index’s quality depends on the classification method, and what the index is worth for a representative marketing decision. In a simulation of 6,000 reviews with known sentiment and 104 weeks of sales driven by sentiment with a two-week lag, a machine-learning classifier that handles negation attains 73% accuracy and an AUC of 0.81, against 64% for a lexicon count. The ML sentiment index correlates 0.85 with sales two weeks ahead, against 0.73 for the lexicon index, and the correlation peaks at the true two-week lead for both. Including the sentiment index in a two-week-ahead sales forecast reduces error by 47%. The paper sets out a pipeline from text to decision, with classification, aggregation, lead-structure estimation and forecast integration as its stages, discusses the strategic decisions the index supports, including launch timing, pricing response and crisis detection, and identifies the data and governance conditions on which its value depends.