Digital Emotions using Sentiment Analysis for Predictive Insights on Customer Recommendations

In an era of unprecedented digital communication and information sharing, understanding the sentiment expressed in textual data is of paramount importance. Sentiment analysis, a subfield of natural language processing, has gained significant prominence due to its potential to decipher the emotions, opinions, and attitudes conveyed through words. This research paper embarks on a journey into the realm of sentiment analysis, where we explore the nuanced interplay of words and emotions in the digital landscape. Electronic commerce (e-commerce) becomes one of the subjects most learned in the education and literature fields of the present day. Superior research in the field electronics focuses mainly on developed economies. From the e-commerce view, the experience of customers has shifted from visiting retail shops to online shopping, making the analysis of customer behavior more salient. In this dataset, the review is taken into account in this sentiment analysis, considering, executing, and analyzing effective ways. Most probabilistic models are used in current research ventures, in the area of text classification, and information retrieval, among other contexts. In turn, ecommerce has grown much broader with online shopping, which is a core platform through which shoppers may be found. In the same breath, it is very imperative to forecast the level of transactions that will be facilitated through e-commerce. This paper will focus on identifying broad patterns in the written opinions of the consumers by using Natural Language Processing (NLP) techniques. The objective of this research is to predict whether customers will recommend the product they purchased by using the information in review text.