scholarly journals Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

2016 ◽  
Vol 8s1 ◽  
pp. BII.S37791 ◽  
Author(s):  
Manabu Torii ◽  
Sameer S. Tilak ◽  
Son Doan ◽  
Daniel S. Zisook ◽  
Jung-wei Fan
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Consumer Product ◽  
Future Research ◽  
Product Reviews ◽  
Learning Tools ◽  
Text Analytics ◽  
Related Information ◽  
Online Product Reviews ◽  
Health Related

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

scholarly journals Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

2021 ◽  
Author(s):  
Michael Saidani ◽  
Harrison Kim ◽  
Nawres Ayadhi ◽  
Bernard Yannou
Keyword(s):  
Language Processing ◽  
Sustainable Design ◽  
Product Category ◽  
Future Research ◽  
Product Reviews ◽  
Learning Tools ◽  
Product Categories ◽  
Sustainable Products ◽  
Online Product Reviews ◽  
Do So

Abstract Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: “What sustainable design insights can be identified or interpreted from online product reviews?”. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.

scholarly journals Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

2021 ◽  
Author(s):  
Michael Saidani ◽  
Harrison Kim ◽  
Bernard Yannou
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Ad Hoc ◽  
Sustainable Design ◽  
Training Model ◽  
Future Research ◽  
Product Reviews ◽  
Sustainable Products ◽  
Review Mining ◽  
Model Training

Abstract The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks — including Python libraries, packages, as well as state-of-the-art algorithms like BERT — are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.

scholarly journals Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

2019 ◽  
Vol 9 (24) ◽  
pp. 5462 ◽  
Author(s):  
Priya Chakriswaran ◽  
Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Vishal Sharma ◽  
Chuan-Yu Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Future Research ◽  
Support Vector ◽  
Product Reviews ◽  
Future Research Directions ◽  
Subject Areas ◽  
Short Time ◽  
The Given

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

Towards Extracting Affordances From Online Consumer Product Reviews

2014 ◽  
Author(s):  
Amanda Chou ◽  
L. H. Shu
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Online Reviews ◽  
Consumer Product ◽  
Product Reviews ◽  
Word Sense ◽  
Online Consumer Reviews ◽  
Online Product Reviews ◽  
Sense Disambiguation ◽  
Online Consumer

We examined online product reviews as a source of novel affordances. Certain affordances may only be discovered through extended use across various environments. User-generated reviews may thus contain unique insights. We analyzed online consumer product reviews from Canadian Tire, one of Canada’s largest retailers. We determined properties of this collection of reviews and commonalities between valuable reviews. In addition to typical challenges associated with natural-language processing, e.g. word-sense disambiguation, we identify characteristics of online consumer reviews that create additional challenges. These challenges include the use of ‘wild English’ and sarcasm in online reviews. We first present criteria to define and more objectively identify novel affordances from review content. Next, k-means clustering reveals that a combination of syntactical features and high frequency word percentages can separate descriptive from non-descriptive review content. Finally, we identified cue phrases that may indicate higher likelihood of affordance content in a review. Despite existing obstacles, the substantial volume of available online product reviews has potential to become a valuable source of affordances and feedback for designers and retailers alike.

scholarly journals Text analysis on health product reviews using r approach

2020 ◽  
Vol 18 (3) ◽  
pp. 1303
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Aliman
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Unstructured Data ◽  
Product Reviews ◽  
Health Product ◽  
Text Analytics ◽  
Data Set ◽  
Web Based ◽  
The Social ◽  
Key Techniques

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

A Survey on Intelligence Tools for Data Analytics

2021 ◽  
pp. 73-95
Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Real Life ◽  
Learning Tools ◽  
The Core ◽  
Training Mode ◽  
Real Life Situation ◽  
Selection Of

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Mining of Medical Trends Using Social Networks

2016 ◽  
pp. 164-182
Author(s):  
Shruti Kohli ◽  
Sonia Saini
Keyword(s):  
Social Networks ◽  
Health Information ◽  
Language Processing ◽  
Social Intelligence ◽  
Medical Information ◽  
Related Information ◽  
Public Health Information ◽  
Health Related ◽  
Post Marketing ◽  
The Media

Recent work in machine learning and natural language processing has studied the content of health related information in tweets and demonstrated the potential for extracting useful public health information from their aggregation. Social intelligence derived from health content has become of significant importance for various applications, including post-marketing drug surveillance, competitive intelligence, medicine reviews and to assess health-related opinions and sentiments. Further, the quantity of medical information in the media such as tweets on Twitter, Facebook or medical blogs is growing at an exponential rate. Medical data such as health records, drug data, etc. has become major candidates for Big Data analysis and thus exploring this content has become a necessity for organizations. However, the volume, velocity, variety, and quality of online health information present challenges, necessitating enhanced facilitation mechanisms for medical social computing. The objective of this chapter is to discuss the possibility of mining medical trends using Social Networks.

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