Online Product Reviews and Their Impact on Third Party Sellers Using Natural Language Processing

Kunathikornkit

International Journal of Business Intelligence Research

et al. 2022

Consumer journey analysis led to efficient marketing implementation. A journey represents a path of steps and interaction between consumer and service units at each touchpoint. Dissatisfaction in the touchpoint, causes a negative effect to retain a customer. Previous studies always constructed the journey maps relied on the narrative approach. According to use Google, consumers always face massive websites to access, which is a pain point in the journey. Improving consumer buying, led to the research aims: identifying consumer needs, and reducing SEO pain-point using content relevance indexing. The data (social media posts from the Thai beauty communities in the year 2020) is analyzed and has found that there are two need types: curative and preventive. The study can segment the 150 websites into four groups which reduce the search space. Moreover, the significant words from the wrapping technique can use to create keywords in the homepage introduction that are matching the products to consumer needs.

Section: Text Processingmentioning

confidence: 99%

“…Due to the data problems: high-dimensional, sparse, and redundant, using the feature warping paradigm is leads to overcome the issues (Akash et al, 2021). The feature warping idea is conducted on the bottom-up management by aggregating the features that highly correlate to be the same group before further process.…”

Section: Feature Wrappingmentioning

confidence: 99%

Facial Skincare Journey

Kunathikornkit

International Journal of Business Intelligence Research

et al. 2022

“…This is because when buying products online, the product cannot be touched or sensed physically, therefore the only way to make the customers trust and buy a product is the feedback or reviews given by the other customers who have bought the product earlier. Nellutla et al [6] mentioned that a typical customer goes to the website, selects a product, inspects the prices and ratings, reads the reviews, and then proceeds with the transaction. However, e-commerce has its own set of issues.…”

Section: E-commercementioning

confidence: 99%

An Empirical Study: Automating e-Commerce Product Rating Through an Analysis of Customer Review

Rajendran,

Abdullah,

Aziz

et al. 2023

IJACSA

Commerce today is a remarkable experience. However, finding and purchasing a right quality product based on numerous product reviews and manual rating in the ecommerce websites utilize much time among the consumers. This paper presents the problems faced by the consumers when buying products in e-commerce websites and a solution to solve the problems. Thus, the idea of an automated product rating system would be very useful for the consumers in which it rates the products automatically based on the reviews given by the buyers. To do this, a technique called Sentiment Analysis is used. It also ranks the products in order based on the product rating that is generated automatically. It would provide a way for the consumers to purchase their desired product within minutes. Surveys and interviews were conducted to find out the problems faced by consumers when purchasing a product online through ecommerce websites. There was also research conducted to study the product rating and product review section on the current ecommerce websites. To conclude, this automated product rating system eventually eases the consumers' effort and time from reading numerous reviews and trusting inaccurate product rating to find a best quality product for them.

“…The study on online product reviews from Flipkart and Amazon employed sentiment analysis and a bag of words model to assess the impact on third-party sellers. Categorizing reviews and conducting topic modeling, the findings emphasized the importance of considering both product and seller reviews for a seamless delivery and defect-free product, benefiting consumers and sellers alike [142]. Introducing a novel approach for computing reputation scores, the paper utilized a BiLSTM, Recurrent Neural Network (RNN) and NLP techniques to analyze textual opinions on online platforms like IMDB and Amazon.…”

Section: Marketing and Brand Managementmentioning

confidence: 99%

“…Aspect-Based Sentiment Analysis offers important opportunities for research and enhancement. Despite advances like [112] and [142], approaches for collecting embedded opinions about product and service attributes remain difficult. Future research should focus on improving ABSA procedures for e-commerce and tourism, which need a deep grasp of customer attitudes.…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.