scholarly journals Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

2020 ◽  
Vol 12 (20) ◽  
pp. 8683 ◽  
Author(s):  
Ayat Zaki Ahmed ◽  
Manuel Rodríguez-Díaz
Keyword(s):  
Sentiment Analysis ◽  
Information Service ◽  
Customer Experience ◽  
Communication Strategy ◽  
Customer Reviews ◽  
Quantitative Variable ◽  
Service Companies ◽  
Essential Tool ◽  
Online Customer Reviews ◽  
Online Comments

Sentiment analysis is becoming an essential tool for analyzing the contents of online customer reviews. This analysis involves identifying the necessary labels to determine whether a comment is positive, negative, or neutral, and the intensity with which the customer’s sentiment is expressed. Based on this information, service companies such as airlines can design and implement a communication strategy to improve their customers’ image of the company and the service received. This study proposes a methodology to identify the significant labels that represent the customers’ sentiments, based on a quantitative variable, that is, the overall rating. The key labels were identified in the comments’ titles, which usually include the words that best define the customer experience. This database was applied to more extensive online customer reviews in order to validate that the identified tags are meaningful for assessing the sentiments expressed in them. The results show that the labels elaborated from the titles are valid for analyzing the feelings in the comments, thus, simplifying the labels to be taken into account when carrying out a sentiment analysis of customers’ online comments.

Reviews’ length and sentiment as correlates of online reviews’ ratings

2018 ◽  
Vol 28 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Maryam Ghasemaghaei ◽  
Seyed Pouyan Eslami ◽  
Ken Deal ◽  
Khaled Hassanein
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Marketing Strategies ◽  
Insurance Companies ◽  
Online Review ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Service Companies ◽  
Consumer Review

Purpose The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the emotions embedded in online reviews and how they associate with specific words used in such reviews. Design/methodology/approach A panel data set of customer reviews was collected for auto, life, and home insurance from January 2012 to December 2015 using a web scraping technique. Using a sentiment analysis approach, 1,584 reviews for the auto, home, and life insurance services of 156 insurance companies were analyzed. Findings The results indicate that, since 2013, consumers have generally had more negative emotions than positive ones toward insurance services. The results also show that consumer review sentiment correlates positively and review length correlates negatively with consumer online review ratings. Furthermore, a two-way ANOVA analysis shows that, in general, short reviews with positive sentiment are associated with high review ratings. Practical implications The findings of this study provide service companies, in general, and insurance companies, in particular, with important guidelines that should be considered to increase consumers’ positive attitude toward their services. Originality/value This paper highlights the importance of sentiment analysis in identifying consumer reviews’ emotions and understanding the associations and interactions of reviews’ length and sentiment on online review rating, which can lead to improved marketing strategies.

A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Consumer Satisfaction ◽  
Data Driven ◽  
Data Sets ◽  
Product Usage ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Data Driven Approach ◽  
Usage Context

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.

scholarly journals Reading Between the Lines: Understanding Customer Experience With Disruptive Technology Through Online Reviews

2021 ◽  
pp. 183933492199948
Author(s):  
Jeandri Robertson ◽  
Caitlin Ferreira ◽  
Jeannette Paschen
Keyword(s):  
Text Analysis ◽  
Online Reviews ◽  
Customer Experience ◽  
Disruptive Technology ◽  
Word Choice ◽  
Emotional Tone ◽  
Customer Reviews ◽  
Brand Engagement ◽  
Online Customer Reviews ◽  
Computerized Text Analysis

A customer’s experience with a brand, as evidenced in online customer reviews, has attracted multidisciplinary scholarly attention. Customer experience plays an important role as an antecedent to brand engagement, brand adoption, and eventual brand loyalty. Thus, it is important for businesses to understand their customers’ experiences so that they can make changes as necessary. The COVID-19 pandemic has brought unprecedented changes to the business landscape, forcing businesses to move online, with many utilizing enterprise video conferencing (EVC) to maintain daily operations. To ensure efficient digitization, many turned to the online reviews of others’ experiences with EVC before engaging with it themselves. This research examined how the customer experience is portrayed through emotional tone and word choice in online reviews for the EVC platform Zoom. Using computerized text analysis, key differences were found in the emotional tone and word choice for low- and high-rated reviews. The complexity and emotionality expressed in reviews have implications on the usability of the review for others. The results from this study suggest that online customer reviews with a high rating express a higher level of expertise and confidence than low-rated reviews. Given the potential dissemination and impact, digital marketers may be well advised to first and foremost respond to online reviews that are high in emotional tone.

scholarly journals Research on the Evaluation of Customer requirement Importance of Smart Products Based on Online Comments and Improved Quantitative Kano Model

CONVERTER ◽  
2021 ◽  
pp. 382-392
Author(s):  
Hang Liu, Zan Ren, Yingjie Li
Keyword(s):  
Customer Requirement ◽  
Kano Model ◽  
Customer Reviews ◽  
Technological Differences ◽  
Online Customer Reviews ◽  
Smart Products ◽  
Online Comments ◽  
Product Manufacturing ◽  
Product Homogeneity

With the development and popularization of smart products, the technological differences of products are decreasing, and the phenomenon of product homogeneity is becoming more and more obvious. It is necessary for the smart product manufacturing firms have the capability to analyze customer requirement deeply and adapt to the dynamically changing market quickly. Therefore, the traditional technology-oriented product development model is no longer suitable for manufacturers to obtain a competitive advantage. Based on this, this paper proposed a method to evaluate the importance of customer demands based on online comments and quantitative Kano model. First, the Python crawler tool is used to obtain online customer reviews of relevant products and the word segmentation processing is performed to obtain the product features and frequency that customers are mainly concerned about, and then the initial importance of demand can be calculated. Furthermore, use the quantitative Kano model to determine the customer satisfaction and revise the initial importance of the requirements to obtain a more reasonable ranking of the importance of user needs. Finally, a case study is carried out with the smart bracelet as an example to verify the effectiveness and feasibility of the model proposed in this paper.

scholarly journals Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews

2021 ◽  
Vol 13 (22) ◽  
pp. 12699
Author(s):  
Xiaobin Zhang ◽  
Hak-Seon Kim
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Linear Regression Analysis ◽  
Customer Experience ◽  
Future Research ◽  
Significant Information ◽  
Customer Reviews ◽  
Online Customer Reviews

Online customer reviews have become a significant information source for scholars and practitioners to understand customer experience and its association with their satisfaction to maintain the sustainable development of relative industries. Thus, this study attempted to find the underlying dimensionality in online customer reviews reflecting customers experience in the Hong Kong Disneyland hotel and identified its relationship with customer satisfaction. Semantic network analysis by Netdraw and factor analysis and linear regression analysis by SPSS 26.0 (IBM, New York, NY, USA) were applied for data analysis. As a result, 70 keywords with high frequency were extracted, and their connection to each other was calculated based on their centralities. Consequently, seven factors were explored by exploratory factor analysis, and moreover, three factors, “Family Empathy”, “Value”, and “Food Quality”, were testified to be negatively related to customer satisfaction. The findings of this study, to a great extent, could be utilized as a research scheme for future research to investigate theme hotels with big data analytics of online customer reviews. More importantly, some new insights and practical implications for the future research and industry development were provided and discussed as well.

Do online consumer reviews help to evaluate the performance of automobile manufacturers?

2020 ◽  
Vol 33 (5) ◽  
pp. 1153-1198
Author(s):  
Amit Singh ◽  
Mamata Jenamani ◽  
Jitesh Thakkar
Keyword(s):  
Sentiment Analysis ◽  
Data Set ◽  
Pareto Chart ◽  
Content Type ◽  
Customer Reviews ◽  
Online Consumer Reviews ◽  
Online Customer Reviews ◽  
Fishbone Diagram ◽  
Online Consumer ◽  
Practical Implications

PurposeThis research proposes a text analytics–based framework that examines the utility of online customer reviews in evaluating automobile manufacturers and discovering their consumer-perceived weaknesses.Design/methodology/approachThe proposed framework integrates aspect-level sentiment analysis with the house of quality (HoQ), TOPSIS, Pareto chart and fishbone diagram. While sentiment analysis mines and quantifies review-embedded consumer opinions on various automobile attributes, the integrated HoQ-TOPSIS analyzes the quantified opinions and evaluates the manufacturers. The Pareto charts assist in discovering consumer-perceived weaknesses of the underperforming manufacturers. Finally, the fishbone diagram visually represents the results in the form with which the manufacturing community is acquainted.FindingsThe proposed framework is tested on a review data set collected from CarWale, a well-known car portal in India. Selecting five manufacturers from the mid-size car segment, the authors identified the worst-performing one and discovered its weak attributes.Practical implicationsThe proposed framework can help the manufacturers in evaluating competitor; identifying consumers' contemporary interests; discovering own and their competitors' weak attributes; assessing the suppliers and sending early warnings; detecting the hazardous defects. It can assist the component suppliers in devising process improvement strategies; improving their customer network; comparing them with competitors. It can support the customers in identifying the best available alternative.Originality/valueThe proposed framework is first of its kind to integrate the sentiment analysis with (1) HoQ-TOPSIS to assess the manufacturers; (2) Pareto chart to discover their weaknesses; (3) fishbone diagram to visually represent the results.

Sentimental Analysis of Online Reviews Using Fuzzy Sets and Rough Sets

2017 ◽  
pp. 376-394
Author(s):  
Anuradha Jagadeesan ◽  
Amit Patil
Keyword(s):  
Fuzzy Sets ◽  
Sentiment Analysis ◽  
Rough Sets ◽  
Opinion Mining ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Excellent Source ◽  
The Web

With the increased interest of online users in E-commerce, the web has become an excellent source for buying and selling of products online. Customer reviews on the web help potential customers to make purchase decisions, and for manufacturers to incorporate improvements in their product or develop new marketing strategies. The increase in customer reviews of a product influence the popularity and the sale rate of the product. This lead to a very important question about the analysis of the sentiments (opinions) expressed in the reviews. As such internet does not have any quality control over customer reviews and it could vary in terms of its quality. Also the trustworthiness of the online reviews is debatable. Sentiment Analysis (SA) or Opinion Mining is the computational analysis of opinions, sentiments, emotions and subjectivity of text. In this chapter, we take a look at the various research challenges and a new dimension involved in sentiment analysis using fuzzy sets and rough sets.

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