If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (email available below). If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. in the wake of finishing Limbo and close to finishing Wordify. You can help adding them by using this form. With bubbling about, now seems like a very good time to dive further into the series. We have no bibliographic references for this item. It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. When requesting a correction, please mention this item's handle: RePEc:oup:jconrs:v:48:y:2021:i:3:p:394-414. You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. 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. 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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