Data analysis of the text of e-commerce reviews is an important tool for all e-commerce companies because it can help them identify and analyze customer needs and desires, thus becoming a valuable tool for improving the product or service. In addition, it is also a vital tool in finding out what the customers are actually really looking for in a product or service, and what is really going on in their heads. In this way, data analysis can be an effective means of understanding a particular part of the text, and provide an explanation about the content of that text. Such a data analysis has the potential to answer many different questions, and can be helpful in understanding product or service quality, customer interest and consumer confidence in e-commerce products and services, as well as the overall quality of the goods and services provided by the retailers, which can contribute to product quality evaluations. Text Summarization and various information retrieval techniques were applied in this study for the testing and evaluation of our methodology for analyzing reviews of e-commerce products. General overview of the corpus was extracted as well as the most important words and keywords will be covered in the results of this study. We also compared various methods for text summarization to evaluate each methods for summarizing large amount of texts containing 100 sentences and above. Finally, we conducted an analysis on the use of text mining techniques to find out and extract relevant information from the dataset.