Amazon Reviews using Sentiment Analysis

The intense competition to attract and maintain customers online is compelling businesses to implement novel strategies to enhance the customer experiences. It is becoming necessary for companies to examine customer reviews on online platforms such as Amazon to understand better how customers rate their products and services. The purpose of this study is to investigate how companies can conduct sentiment analysis based on Amazon reviews to gain more insights into customer experiences. The dataset selected for this capstone consists of customer reviews and ratings from consumer reviews of Amazon products. Amazon product reviews enable a business to gain insights on customer experiences regarding specific products and services. The study will enable companies to pinpoint the reasons for positive and negative customer reviews and implement effective strategies to address them accordingly. The capstone project helps companies use sentiment analysis to understand customer experiences using Amazon reviews

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Online retailers and merchants increasingly request feedback from their clients on the products they purchase. This has led to a significant increase in the number of product reviews posted online, as more people are making purchases online. The opinions expressed in these customer reviews have a significant impact on other customers' purchase decisions, as they are influenced by other customers' recommendations or complaints. This study used Amazon, a well-known and widely used e-commerce platform, to examine sentiment categorization using several machine learning techniques while analyzing an Amazon Reviews dataset. At first, the reviews were transformed into vector representations using the Bag-of-Words approach. Word cloud was used to illustrate the text data in terms of the frequency they appear in the review. Subsequently, the machine learning methods decision trees and logistic regression were used. The two models used in this study achieved high levels of accuracy in analyzing the dataset. Specifically, the Decision Tree model outperformed the Logistic Regression one, achieving an impressive accuracy of 99% compared to the 94% of the latter

Profiling users' behavior, and identifying important features of review 'helpfulness'

The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people’s attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers

Identifying the combinations of motivations and emotions for creating satisfied users in SNSs : An fsQCA approach

acceptedVersio

Klasifikasi kualitas ulasan produk berdasarkan semantic dan structural features menggunakan support vector machine

Ulasan produk merupakan opini tertulis yang disampaikan oleh konsumen dalam menilai suatu produk. Adanya ulasan produk menjadi penting dikarenakan dapat membantu konsumen dalam membuat keputusan pada pembelian produk yang lebih baik. Namun ulasan produk dapat menjadi tidak penting apabila kualitas informasi dari ulasan tersebut tidak bermanfaat. Hal tersebut dapat diminimalisir apabila dilakukan klasifikasi untuk mengetahui ulasan mana yang bermanfaat atau tidak bermanfaat. Agar hal tersebut dapat tercapai, maka pada penelitian ini diterapkan model support vector machine (SVM) berbasiskan pada pengenalan pola menggunakan semantic dan structural features untuk dapat melakukan klasifikasi pada teks ulasan berdasarkan karakteristiknya. Hasil akhir menunjukan bahwa model SVM pada semantic feature memiliki nilai f-measure tertinggi sebesar 0.825. Sedangkan pada structural feature nilai f-measure tertinggi adalah sebesar 0.823. Dari hal tersebut dapat disimpulkan bahwa semantic feature dapat digunakan untuk mengidentifikasi karakteristik dari teks ulasan yang bermanfaat atau tidak bermanfaat dengan baik