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

2021 ◽  
Vol 12 (1) ◽  
pp. 26-47
Author(s):  
Akash Phaniteja Nellutla ◽  
Manoj Hudnurkar ◽  
Suhas Suresh Ambekar ◽  
Abhay D. Lidbe
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Online Reviews ◽  
Third Party ◽  
Bag Of Words ◽  
Product Reviews ◽  
Specific Product ◽  
Online Product Reviews ◽  
The Impact

The purpose of this paper is to gain insights from the online product reviews of e-commerce sites such as Flipkart and Amazon and analyze its impact on third party sellers. To judge the authenticity of a product, reviews are more useful than ratings, since ratings do not give a complete picture. It is always preferred to consider both the product and seller reviews to have a seamless delivery and defect less product. In this paper, natural processing methods are used to gain insights by considering online reviews of a product. Methods such as sentiment analysis, bag of words model help to understand the impact of online product reviews on the seller's ratings and their performance over some time. The reviews are categorized into positive, negative, and neutral using sentiment analysis. Further, topic modeling is done to find out the topic reviews are majorly referring to. The seller reviews for a specific product after analysis are compared with the overall seller reviews to judge the authenticity. The results of this paper would be beneficial to both the consumers and sellers.

Customers' experience of purchasing event tickets: mining online reviews based on topic modeling and sentiment analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krzysztof Celuch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Third Party ◽  
Content Type

PurposeIn search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.Design/methodology/approachThis is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.FindingsThe findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.Originality/valueDrawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

scholarly journals May we buy your love? psychological effects of incentives on writing likelihood and valence of online product reviews

2020 ◽  
Vol 30 (4) ◽  
pp. 805-820
Author(s):  
Ina Garnefeld ◽  
Sabrina Helm ◽  
Ann-Kathrin Grötschel
Keyword(s):  
Experimental Studies ◽  
Online Reviews ◽  
Psychological Effects ◽  
Online Review ◽  
Product Reviews ◽  
Monetary Rewards ◽  
Online Product Reviews ◽  
The Impact

AbstractAcknowledging the impact on their sales, companies strive to increase the number of positive online reviews of their products. A recently popular practice for stimulating online reviews is offering monetary rewards to customers in return for writing an online review. However, it is unclear whether such practices succeed in fulfilling two main objectives, namely, increasing the number and the valence of online reviews. With one pilot and two experimental studies, this research shows that offering incentives indeed increases the likelihood of review writing. However, the effect on review valence is mixed, due to contradictory psychological effects: Incentive recipients intend to reciprocate by writing favorable reviews but also perceive a need to resist marketers’ influence, which negatively affects their review valence. Finally, recipients who are less satisfied with the product are particularly prone to psychological costs and decrease the positivity of their online reviews. Consequently, incentives should be applied carefully.

scholarly journals Data Mining and Machine Learning: Design a Generalized Real Time Sentiment Analysis System on Tweeter Data Using Natural Language Processing

2019 ◽  
Vol 8 (6) ◽  
pp. 2139-2142
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Automated System ◽  
Product Reviews ◽  
Analysis System ◽  
Result Analysis ◽  
Python Programming ◽  
The Impact ◽  
Analyze Data

Sentiment analysis is a task, that is becoming recently important for numerous companies. Because the consigner subscriptions on social media like Facebook, twitter and other side get their product reviews. If the company wants to track tweets about their brand to command over the impact on time or many website analyze the comments on their articles. This will help them to track comments and impact. So the sentiment analysis is an automated system that collects and analyzes the content and generates the desired results. This paper proposes a sentiment analysis system for twitter posts. Proposed system will work on real time tweets. System is also designed in such a way that this can analyze data related to any topic. Python programming language is used to extract tweets form twitter feeds. Proposed system also calculates the level of sentiments. That how much negative or positive tweets are. This paper also presents some real time result analysis.

scholarly journals Occupants’ Satisfaction with LEED- and Non-LEED-Certified Apartments Using Social Media Data

2021 ◽  
Author(s):  
Xingtong Guo ◽  
Kyumin Lee ◽  
Zhe Wang ◽  
Shichao Liu
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
System Development ◽  
Residential Buildings ◽  
Statistical Significance ◽  
Environmental Design ◽  
Online Reviews ◽  
Star Rating

Leadership in Energy and Environmental Design (LEED) certified buildings aim to offer a sustainable and healthy built environment. Previous studies have shown mixed and inconsistent results on whether occupants in LEED-certified buildings are more satisfying than in non-LEED-certified counterparts. Those studies usually based on surveys or questionnaires for commercial buildings were limited by sample size and pre-defined question structures. Since most people stay longer at home during the COVID-19 pandemic and the trend might continue in the post-pandemic era, assessing the satisfaction with LEED-certified residential buildings benefits future environmental design and certification system development. In this work, we propose a natural language processing-based approach for such assessment. The study collected 16,761 online reviews on 260 LEED-certified apartments and 180 non-LEED-certified-apartments from social media, then applied topic modeling and sentiment analysis to evaluate occupants’ satisfaction. Based on topic modeling, we categorized online comments into three topic clusters, 1) location and transportation, 2) running cost, and 3) health and wellbeing. The subsequent sentiment analysis has shown a statistically significant but small or negligible enhancement in the satisfaction occurring in LEED-certified apartments compared to non-LEED-certified ones concerning all the three topic clusters. The “significant but small or negligible uptick” has also been found in online star rating and indoor environmental satisfaction. The only exception with a large effect size is lighting that is significantly more satisfying in LEED-certified apartments. Nevertheless, the statistical significance in online star rating disappears when it is normalized by rent price and property house value.

The role of online product reviews on information adoption of new product development professionals

2015 ◽  
Vol 25 (3) ◽  
pp. 435-452 ◽  
Author(s):  
Kyung Young Lee ◽  
Sung-Byung Yang
Keyword(s):  
New Product Development ◽  
New Product ◽  
The Body ◽  
Product Reviews ◽  
Specific Product ◽  
Content Type ◽  
Online Product Reviews ◽  
The Impact ◽  
Product Rating ◽  
Information Adoption

Purpose – The purpose of this paper is to investigate the impact of features involving online product reviews (OPRs) on information adoption by new product developers (NPDs). Design/methodology/approach – In total, 143 OPRs on a specific product on Amazon.com were collected as the sample of this study. Using content analysis ratings and observed data in OPRs, the research model was analyzed with the partial least squares (PLS) method. Findings – Results suggest that helpfulness rating and the degree of referencing are positively associated with NPDs’ information adoption, while the extremeness of product rating is negatively associated. Moreover, title attractiveness mitigates the negative relationship between the extremeness of product rating and information adoption. Practical implications – The findings provide interesting insight for NPDs who visit e-commerce sites to learn through electronic word-of-mouth (eWOM) communication. OPRs with a higher degree of referencing, higher helpfulness rating, moderate level of product rating, and higher degree of title attractiveness are better adopted by NPDs. Social implications – This paper investigates the value of OPRs for a specific group of information users and suggests that information about products generated by anonymous consumers can be crucial. Originality/value – While extant studies have focussed on the impacts of OPRs on consumers’ purchasing intention and behavior, this paper is among the first attempts to investigate the impacts of OPRs on developers’ information adoption. Therefore, it contributes to the body of knowledge on knowledge transfer from consumers to business as well as the information adoption literature.

Credible Effects: The Impact of Disclosure of Material Connections Within Online Product Reviews

2019 ◽  
Vol 39 (3) ◽  
pp. 353-368
Author(s):  
Michelle D. Steward ◽  
Alvin C. Burns ◽  
Felicia N. Morgan ◽  
Michelle L. Roehm
Keyword(s):  
Federal Trade Commission ◽  
Online Reviews ◽  
Product Reviews ◽  
Online Product Reviews ◽  
The Cost ◽  
The Impact ◽  
First Time ◽  
Source Of Information

Online product reviews are an influential source of information for consumers. With pressures to have readily available reviews, businesses must determine the best strategy for obtaining them. In 2009, for the first time in 29 years, the Federal Trade Commission (FTC) updated endorsement guidelines to address concerns over possible deception within online reviews. Since that time, the FTC has issued four additional statements underlining its concerns and providing additional examples of how to comply. In 2017, the first action of enforcement was made against individuals failing to adhere to these guidelines. In light of the FTC’s guidelines and pressures to have reviews, businesses must ask which type of affiliated reviewers, if any, are the most influential. As to reviewer credibility, the literature offers contradictory predictions. Through three experiments with a total of 1,077 consumers, the authors examine effects of reviewer affiliation. The findings affirm the spirit of the FTC’s updated guidelines. However, affiliation comes at a cost. Depending on the competitive context, the cost may be worth the benefits.

The impact of COVID-19 on online product reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Omer Cem Kutlubay ◽  
Mesut Cicek ◽  
Serdar Yayla
Keyword(s):  
Well Being ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Evaluations ◽  
Content Type ◽  
Online Product Reviews ◽  
Online Product Ratings ◽  
Negative Tone ◽  
The Impact ◽  
Product Ratings

Purpose The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors have caused the psychological well-being of customers to deteriorate. By taking up the role of online reviews in the regulation of consumers’ moods, this study aims to examine the changes that have occurred in online product ratings, as well as the negative tone and word counts of product reviews during the COVID-19 pandemic. Design/methodology/approach This study examines the online reviews of 321 products in the pre-COVID, immediate COVID and extended COVID periods. This paper compares the changes that have taken place in product evaluations via various analysis of variance analyses. The authors also test the effect of COVID-related deaths on product evaluations via regression analyses. Findings The results indicate that online product ratings decreased sharply just after the outbreak of COVID-19. The study also found that the tone of reviews was found to be more negative and the length of reviews appeared to be longer in comparison to the pre-COVID-19 period. The results also revealed that the product type (experience vs search) moderated the effect of the pandemic in online reviews and the impact of COVID-19 on online product reviews diminished in the later stages of the ongoing pandemic. Practical implications Managers should be aware of the detrimental impact of pandemics on online product reviews and be more responsive to customer problems during the early stages of pandemics. Originality/value To the best of the authors’ knowledge, this is the first study that analyzes the effects of a pandemic on online product ratings and review content. As such, this study offers a timely contribution to the marketing literature.

scholarly journals Sentiment Analysis of Online Product Reviews Using DLMNN and Future Prediction of Online Product Using IANFIS

2020 ◽  
Author(s):  
Sasikala p ◽  
Mary Immaculate Sheela
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Weighting Factor ◽  
Product Reviews ◽  
Review Analysis ◽  
Online Product Reviews ◽  
Online Product Review ◽  
Future Prediction ◽  
Respective Product

Abstract Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). It captures the user’s opinion, feelings, and belief regarding the respective product especially to determine whether the user’s attitude is positive, negative, or neutral. This analysis greatly helps the companies to make necessary changes in their product which in return can overcome the flaws that the product is facing and targets better customer satisfaction. Existing techniques for the sentiment analysis of online product reviews obtained low accuracy and also took more time for training. To overcome such issues in this paper, a DLMNN is proposed for sentiment analysis of online product review and IANFIS is proposed for future prediction of online product. Here, the sentiment analysis and future predictions are done on the products taken from the food review dataset. First, from the dataset, the data values are partitioned into GB, CB, and CLB scenarios and then the review analysis for each scenario is performed separately using DLMNN and they give the result as positive, negative, and neutral reviews for the product. After the process of review classification based on these three scenarios, the future prediction of the products is done by performing weighting factor and classification using IANFIS. Experimental results are compared with some existing techniques and the results show that the proposed method outperforms other existing algorithms.

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.

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