Sentimental analysis of customer product Reviews to understand customer needs using machine learning

Abstract

People are influencing aspects of the digital world through machines. As a result, it is crucial to upgrade and use this aspect to do so. In the past, people used written letters to provide feedback. However, people are now posting these reviews to the seller’s page directly on the internet.In the digital age, user feedback, and reviews

have a significant impact on shaping businesses. However, it is challenging to ana- lyze and understand the sentiments conveyed owing to the large volume of data and

the presence of spam.If we can develop automated systems that can interpret senti- ments of people and emotions from user reviews, which would help to leave a great

impact on improving their marketing strategies and can understand the require- ments of customer. However, machines are constrained by binary language, and,

thus faces difficulties in comprehending human emotions and thoughts.By leverag- ing machine learning algorithms for sentiment analysis, we aim to evaluate sentiment

in a vast collection of customer reviews. Sentiment analysis is an essential domain in machine learning and natural language processing, which focuses on identifying and classifying sentiments, opinions, and emotions expressed in textual data. This

paper presents a comprehensive overview of sentiment analysis within the frame- work of machine learning approaches. For sentiment analysis, a wide variety of

machine learning techniques and methods have been studied, including more estab- lished methods like deep learning models such as Convolutional Neural Networks

(CNNs) and Transformers like BERT as well as traditional approaches like Naive Bayes and linear Support Vector Machines (SVM), KNN, and logistic regression. . The paper also addresses the challenges associated with sentimental analysis, such as data preprocessing, extracting features, and selection of models. Furthermore, it emphasizes the significance of labeled data and underscores the role of sentiment lexicons and word embeddings in improving sentiment analysis performance. The

paper concludes by discussing the prospects of sentiment analysis in machine learn- ing, highlighting its significance in social media analysis, customer feedback analysis,

and market research. Therefore, the research outcomes of our paper provide valuable insights for companies that would enable them to enhance their marketing strategies and improve their products to meet customer requirements more effectively based on the evaluation of customer reviews and feedback.