Abstract
Positive 'word of mouth' is the key to successful innovation diffusion. Innovation managers pay close attention to examine customer sentiment. Online reviews are often the most accessible sources of customer feedback. Online review ratings and online review volume, the two most common metrics to interpret customer sentiment from online reviews, have some critical limitations. Online review ratings are prone to extremity bias since extremely satisfied or dissatisfied customers are most likely to leave online reviews. Online review volume can increase for any reason, and it does not necessarily indicate positive or negative customer feedback. This article explores text mining methods and proposes some alternative metrics to interpret customer sentiment. The findings show that sentiment scores might be less prone to extremity bias compared to online review ratings. Sentiment scores tended to fit a normal distribution while online review ratings were skewed to extreme values. Sentiment scores and review lengths, when combined, can provide a new angle to observe enthusiasm.
| Original language | English |
|---|---|
| Pages (from-to) | 162-168 |
| Number of pages | 7 |
| Journal | IEEE Engineering Management Review |
| Volume | 49 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2021 |
Keywords
- Customer reviews
- online reviews
- opinion mining
- sentiment analysis
- text mining