Wordify
A Tool for Discovering and Differentiating Consumer Vocabularies
Bibliographic Data
| ID | 11074452 |
|---|---|
| Authors | Dirk Hovy (0000-0002-4618-3127), Shiri Melumad (0000-0003-0104-6003, corresponding author), J Jeffrey Inman (0000-0003-0410-2242) |
| Editors | Richard J Lutz, Charles F Hofacker (0000-0002-1013-1456) |
| Year | 2021 |
| Volume | 48 |
| Issue | 3 |
| Pages | 394-414 |
| Publication date | 2021-10-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Consumer Research (JOURNAL) |
| Journal identifiers | ISSN: 0093-5301 • E-ISSN: 1537-5277 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/jcr/ucab018 |
| OpenAlex | W3151867831 |
| Language | EN |
| Citations received | 4 |
| References cited | 31 |
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 chi-square selectors, while offering significant advantages in computing time. 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
Data science · Information retrieval · Linguistics · Natural language processing · Probabilistic logic · Sentiment analysis · Topic model · Valence (chemistry) · Word (group theory) · Advanced Text Analysis Techniques · Artificial Intelligence · Computer Science · Digital Marketing and Social Media · Sentiment Analysis and Opinion Mining
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| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |