The primary objective of this paper is to demonstrate the ability of data analytics in capturing and translating consumer opinions into sentiment identification with the application of automation techniques. By applying various data analytic techniques, we propose an effective model to retrieve consumers’ reviews from different platforms and transform them into useful piece of information to support the decision making process in organizations.
This study focuses on the aspects of social content generated by consumers. Web 2.0, and social media platforms have changed the behavior of consumers on the Internet. Consumers are now utilizing these social platforms to express their own experience about products and services. These opinions carry valuable information reflecting on consumer’s response to the products. Consequently, there is a need for business to have an appropriate method to capture information from the source of data.
To address this concern, this paper aims to explore opportunities for automation in the consumer opinion mining and product quality assessment. The design framework is used in conjunction with literature review, theoretical analysis and creative method in order to answer the research question “What is the effect of text mining techniques on quality management of Jabra, using consumers’ product reviews?”
We use the consumer reviews and opinions about Jabra from Amazon and Bestbuy for our data to do the analysis of product features based on the reviews. The main concepts we have used for our research include social data analytics, sentiment analysis and natural language processing. The dataset consists of consumer reviews extracted from the two dominant e-commerce websites, Amazon and Bestbuy. The main tools used were Python and its supported library (Textblob, NLTK, sklearn and statsmodel), SQLite and QlikSense
The result confirms the fact that social data consists of valuable insight from consumers and also for product quality control manager to improve product quality. Therefore, organizations need to focus on developing models based on these reviews to take advantage of the big social data.
|Educations||MSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis|
|Number of pages||126|