WORDIFY BUREAUCRATIC FREE

Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. In this period, the adoption of ML in marketing has grown significantly. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022.

A discussion is also provided on the use of Wordify in conjunction with other text-analysis tools, such as probabilistic topic modeling and sentiment analysis, to gain more profound knowledge of the role of language in consumer behavior.Įven though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. We show empirically that Wordify’s RLR algorithm performs better at discriminating vocabularies than support vector machines and chi-square selectors, while offering significant advantages in computing time.

We present illustrative examples to show how the tool can be used for such diverse purposes as 1) uncovering the distinctive vocabularies that consumers use when writing reviews on smartphones versus PCs, 2) discovering how the words used in Tweets differ between presumed supporters and opponents of a controversial ad, and 3) expanding the dictionaries of dictionary-based sentiment-measurement tools. The tool, Wordify, uses randomized logistic regression (RLR) to identify the words that best discriminate texts drawn from different pre-classified corpora, such as posts written by men versus women, or texts containing mostly negative versus positive valence.

WORDIFY BUREAUCRATIC FREE

This work describes and illustrates a free and easy-to-use online text-analysis tool for understanding how consumer word use varies across contexts.

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