Abstract:
"
In today’s business context most of the businesses have already identified that the
customer feedback reviews play a major part in terms of the businesses next steps.
Businesses are also aware that there are many important business analytics which could
be found through analyzing the customer reviews. Currently even though most of the
businesses encourage their customers to post their experiences regarding the business
products / services, they still analyze and evaluate those reviews manually. This manual
process is time consuming, error prone and the quality of the analytics would depend on
the experience level of the person who evaluates the reviews.
This research focuses on design and development of “Customer Feedback Analyzer”
which provides tool-based support to analyze the text-based customer reviews and then
showcase important business analytics eliminating the need to manually evaluate and
analyze the customer reviews. Tool is empowered with data scraping, preprocessing and
finally classifying the reviews using sentiment analysis technologies in order derive
important business analytics. Tool uses multiple data preprocessing techniques such as
sentence tokenization, lemmatization and stop word removal combined together. Text based customer reviews are classified in to three different groups as “positive”,
“negative” and “mixed” reviews. Finally, as the outcome, tool provides overall business
analytics as well as analytics upon business products / services.
"
