Wordify: A Tool for Discovering and Differentiating Consumer Vocabularies
Journal of Consumer Research, ISSN: 0093-5301, Vol: 48, Issue: 3, Page: 394-414
2021
- 16Citations
- 99Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This work describes and illustrates a free and easy-to-use online text-analysis tool for understanding how consumer word use varies across contexts. 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. 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. We show empirically that Wordify's RLR algorithm performs better at discriminating vocabularies than support vector machines and chisquare selectors, while offering significant advantages in computing time. A discussion is also provided on the use of Wordify in conjunction with other textanalysis tools, such as probabilistic topic modeling and sentiment analysis, to gain more profound knowledge of the role of language in consumer behavior.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85116438673&origin=inward; http://dx.doi.org/10.1093/jcr/ucab018; https://academic.oup.com/jcr/article/48/3/394/6199426; http://academic.oup.com/jcr/article-pdf/48/3/394/40853499/ucab018.pdf; http://academic.oup.com/jcr/advance-article-pdf/doi/10.1093/jcr/ucab018/37371481/ucab018.pdf; https://dx.doi.org/10.1093/jcr/ucab018; https://academic.oup.com/jcr/article-abstract/48/3/394/6199426?redirectedFrom=fulltext
Oxford University Press (OUP)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know