We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Our approach was evaluated used healthcare text downloaded from, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. ![]() Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.Ĭlear language makes communication easier between any two parties. 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. 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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