The method of automatically extracting the important sentences from input materials in order to properly summarize the document is known as automatic text summarization, and it offers a potential remedy for the problem of information overload. The only words or phrases used in extractive text summarizing techniques are those from the source text. The current study uses a classification strategy to perform an analysis of Telugu Amazon reviews. Three stages of Result of the study are involved: Analyzing the semantics of preprocessed data, categorizing it, and classifying it. Language processing using natural language (NLP) sentiment analysis is a challenging task that distributes with unstructured textual input and categorizes it as either a great, awful, or neutral sentiment. The part of text mining known as sentiment and Giture selection with Adaptive Boostinger classifier, analysis aims to explain sentiments, the ideas, and arrogances expressed in a text or piece of textual content. Entropy Index feauture using the Hybrid asemantic analysis is also carried out to determine Feelings scores of the each review has a compound polarity. In comparison to the current convolutional neural networks and Hybrid approach to query selection, which achieved 77.9 and 73.3% accuracy, the suggested Selecting features based on a hybrid entropy-Gini index achieved 98.12% accuracy.