Database Reference
In-Depth Information
indexed reviews. This step corresponds to Phases 2 and 3 of the Data
Analytic Lifecycle.
3. Compute the usefulness of each word in the reviews using methods such
as TFIDF (Section 9.5). This and the following two steps correspond to
Phases 3 through 5 of the Data Analytic Lifecycle.
4. Categorize documents by topics (Section 9.6). This can be achieved
through topic models (such as latent Dirichlet allocation).
5. Determine sentiments of the reviews (Section 9.7). Identify whether the
reviews are positive or negative. Many product review sites provide ratings
of a product with each review. If such information is not available,
techniques like sentiment analysis can be used on the textual data to infer
the underlying sentiments. People can express many emotions. To keep
the process simple, ACME considers sentiments as positive, neutral, or
negative.
6. Review the results and gain greater insights (Section 9.8). This step
corresponds to Phase 5 and 6 of the Data Analytic Lifecycle. Marketing
gathers the results from the previous steps. Find out what exactly makes
people love or hate a product. Use one or more visualization techniques to
report the findings. Test the soundness of the conclusions and
operationalize the findings if applicable.
Figure 9.1
ACME's Text Analysis Process
This process organizes the topics presented in the rest of the chapter and calls out
some of the difficulties that are unique to text analysis.
