scholarly journals ANALYSIS OF CUSTOMER SENTIMENT ON PRODUCT FEATURES AFTER THE OUTBREAK OF CORONAVIRUS DISEASE (COVID-19) BASED ON ONLINE REVIEWS

2021 ◽  
Vol 1 ◽  
pp. 457-466
Author(s):  
Jinju Kim ◽  
Seyoung Park ◽  
Harrison Kim
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis ◽  
Global Economy ◽  
Dynamic Change ◽  
Online Reviews ◽  
Dynamic Changes ◽  
Supply Chain Disruptions ◽  
Customer Preferences ◽  
Product Features ◽  
Purchasing Patterns

AbstractThe outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.

scholarly journals Algorithm for extracting product feature from e-commerce comment

2021 ◽  
Vol 22 (2) ◽  
pp. 1199
Author(s):  
Chanida Kaewphet ◽  
Nawaporn Wisitpongpun
Keyword(s):  
Feature Extraction ◽  
Mobile Phone ◽  
Word Frequency ◽  
Hybrid Approach ◽  
Online Reviews ◽  
The Other ◽  
Automatic Extraction ◽  
Purchasing Decisions ◽  
Product Features ◽  
Different Types

<span>Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.</span>

scholarly journals Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Najla M. Alharbi ◽  
Norah S. Alghamdi ◽  
Eman H. Alkhammash ◽  
Jehad F. Al Amri
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Online Reviews ◽  
Word Embedding ◽  
Learning Approaches ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Consumer feedback is highly valuable in business to assess their performance and is also beneficial to customers as it gives them an idea of what to expect from new products. In this research, the aim is to evaluate different deep learning approaches to accurately predict the opinion of customers based on mobile phone reviews obtained from Amazon.com. The prediction is based on analysing these reviews and categorizing them as positive, negative, or neutral. Different deep learning algorithms have been implemented and evaluated such as simple RNN with its four variants, namely, Long Short-Term Memory Networks (LRNN), Group Long Short-Term Memory Networks (GLRNN), gated recurrent unit (GRNN), and update recurrent unit (UGRNN). All evaluated algorithms are combined with word embedding as feature extraction approach for sentiment analysis including Glove, word2vec, and FastText by Skip-grams. The five different algorithms with the three feature extraction methods are evaluated based on accuracy, recall, precision, and F1-score for both balanced and unbalanced datasets. For the unbalanced dataset, it was found that the GLRNN algorithms with FastText feature extraction scored the highest accuracy of 93.75%. This result achieved the highest accuracy on this dataset when compared with other methods mentioned in the literature. For the balanced dataset, the highest achieved accuracy was 88.39% by the LRNN algorithm.

scholarly journals Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

2020 ◽  
Vol 47 (2) ◽  
pp. 105-121
Author(s):  
Wie Wei ◽  
Yi-Ping Liu ◽  
Lei-Ru Wei
Keyword(s):  
Sentiment Analysis ◽  
Academic Research ◽  
Online Reviews ◽  
Domain Ontology ◽  
Business Strategies ◽  
Product Reviews ◽  
Analysis Framework ◽  
Fine Grained ◽  
Product Features ◽  
Sentiment Dictionary

Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.

Improving the Accuracy and Diversity of Feature Extraction From Online Reviews Using Keyword Embedding and Two Clustering Methods

2020 ◽  
Author(s):  
Seyoung Park ◽  
Harrison M. Kim
Keyword(s):  
Feature Extraction ◽  
Product Development ◽  
Product Design ◽  
Spectral Clustering ◽  
Online Reviews ◽  
Clustering Methods ◽  
Online Data ◽  
Product Features ◽  
Noise Filter ◽  
Alternative Research

Abstract In product design, it is essential to understand customers’ preferences for product features. Traditional methods including the survey and interview are time-consuming and costly. As an alternative, research on utilizing online data for user analysis has been actively conducted. Although various methods have been proposed in this domain, most of them focus on the main features or usages of the product. However, from the manufacturer’s perspective, sub-features are as crucial as main features or usages, because the preference for sub-features is necessary for component configuration in actual product development. As the first step to solve this problem, this paper proposes a methodology to extract and cluster sub-features by incorporating phrase embedding into the previous word embedding. Also, the presented methodology increases the accuracy and diversity of the clustering result by using X-means clustering as a noise filter and adopting spectral clustering.

Phrase Embedding and Clustering for Sub-Feature Extraction from Online Data

2021 ◽  
pp. 1-11
Author(s):  
Seyoung Park ◽  
Harrison Kim
Keyword(s):  
Feature Extraction ◽  
High Accuracy ◽  
Clustering Methods ◽  
Online Data ◽  
Practical Applications ◽  
Customer Preference ◽  
Customer Preferences ◽  
Embodiment Design ◽  
Product Features ◽  
Efficient Resource

Abstract Recently, online user-generated data has been used as an efficient resource for customer analysis. In the product design area, various methods for analyzing customer preference for product features have been suggested. However, most of them focused on feature categories rather than product components which are crucial in practical applications. To address that limitation, this paper proposes a new methodology for extracting part-level features from online data. First, the method detects phrases in the data and filtered them using product manual documents. The filtered phrases are embedded into vectors, and then they are divided into several groups by two clustering methods. The resulting clusters are labeled by analyzing items in each cluster. Finally, cue phrases for sub-features are obtained by selecting clusters with labels representing product features. The proposed methodology was tested on smartphone review data. The result provides feature clusters containing sub-feature phrases with high accuracy. The obtained cue phrases will be used in analyzing customer preferences for part-level features and this can help product designers determine the optimal component configuration in embodiment design.

scholarly journals Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

2019 ◽  
Vol 11 (15) ◽  
pp. 4235 ◽  
Author(s):  
Kauffmann ◽  
Peral ◽  
Gil ◽  
Ferrández ◽  
Sellers ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Brand Management ◽  
Online Reviews ◽  
Sustainable Competitive Advantage ◽  
Text Data ◽  
Text Data Mining ◽  
Product Features

Companies have realized the importance of “big data” in creating a sustainable competitive advantage, and user-generated content (UGC) represents one of big data’s most important sources. From blogs to social media and online reviews, consumers generate a huge amount of brand-related information that has a decisive potential business value for marketing purposes. Particularly, we focus on online reviews that could have an influence on brand image and positioning. Within this context, and using the usual quantitative star score ratings, a recent stream of research has employed sentiment analysis (SA) tools to examine the textual content of reviews and categorize buyer opinions. Although many SA tools split comments into negative or positive, a review can contain phrases with different polarities because the user can have different sentiments about each feature of the product. Finding the polarity of each feature can be interesting for product managers and brand management. In this paper, we present a general framework that uses natural language processing (NLP) techniques, including sentiment analysis, text data mining, and clustering techniques, to obtain new scores based on consumer sentiments for different product features. The main contribution of our proposal is the combination of price and the aforementioned scores to define a new global score for the product, which allows us to obtain a ranking according to product features. Furthermore, the products can be classified according to their positive, neutral, or negative features (visualized on dashboards), helping consumers with their sustainable purchasing behavior. We proved the validity of our approach in a case study using big data extracted from Amazon online reviews (specifically cell phones), obtaining satisfactory and promising results. After the experimentation, we could conclude that our work is able to improve recommender systems by using positive, neutral, and negative customer opinions and by classifying customers based on their comments.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 471
Author(s):  
Bo Mu ◽  
Guohang Tian ◽  
Gengyu Xin ◽  
Miao Hu ◽  
Panpan Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Nearest Neighbor ◽  
Dynamic Change ◽  
Henan Province ◽  
Central China ◽  
Node Degree ◽  
Human Society ◽  
Stepping Stones ◽  
Dynamic Changes ◽  
Graph Metrics

An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results revealed that the water area decreased significantly during the periods of 1990–2000 and 2010–2018 due to a decrease in tidal flats and linear waters, but increased significantly in 2000–2010 due to an increase in patchy waters. Human construction activities, socioeconomic development and topography were the key factors driving the dynamics of water pattern and connectivity. The use of graph metrics (node degree, betweenness centrality, and delta probability of connectivity) in combination with landscape metrics (Euclidean nearest-neighbor distance) can help establish the parameters of threshold distance between connected habitats, identify hubs and stepping stones, and determine the relatively important water patches that require priority protection or development.

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