Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews

2020 ◽  
pp. 1-26
Author(s):  
Simran Sidhu ◽  
Surinder Singh Khurana
Keyword(s):  
Sentiment Analysis ◽  
Public Perception ◽  
Online Reviews ◽  
Online Review ◽  
University Rankings ◽  
Ranking List ◽  
The Public ◽  
Social Media Platforms ◽  
Good Potential ◽  
Sentiment Score

A large number of reviews are expressed on academic institutes using the online review portals and other social media platforms. Such reviews are a good potential source for evaluating the Indian academic institutes. This chapter aimed to collect and analyze the sentiments of the online reviews of the academic institutes and ranked the institutes on the basis of their garnered online reviews. Lexical-based sentiment analysis of their online reviews is used to rank academic institutes. Then these rankings were compared with the NIRF PR Overall University Rankings List 2017. The outcome of this work can efficiently support the overall university rankings of the NIRF ranking list to enhance NIRF's public perception parameter (PRPUB). The results showed that Panjab University achieved the highest sentiment score, which was followed by BITS-Pilani. The results highlighted that there is a significant gap between NIRF's perception rankings and the perception of the public in general regarding an academic institute as expressed in online reviews.

FLORA: a hierarchical fuzzy system for online accommodation review analysis

2019 ◽  
Vol 21 (3) ◽  
pp. 347-367
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Online Reviews ◽  
Online Review ◽  
Probability Weighting ◽  
Content Type ◽  
Analysis Process ◽  
Review Analysis ◽  
Accuracy Comparison ◽  
Hierarchical Fuzzy System

Purpose This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

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.

Product weakness finder: an opinion-aware system through sentiment analysis

2014 ◽  
Vol 114 (8) ◽  
pp. 1301-1320 ◽  
Author(s):  
Hongwei Wang ◽  
Wei Wang
Keyword(s):  
Sentiment Analysis ◽  
Comparative Evaluation ◽  
Analytical Framework ◽  
Online Reviews ◽  
Product Reviews ◽  
Network Algorithms ◽  
Content Type ◽  
Sentiment Mining ◽  
Highly Correlated ◽  
Sentiment Score

Purpose – Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way. Design/methodology/approach – PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations. Findings – The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy. Research limitations/implications – Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework. Practical implications – The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies. Social implications – A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare. Originality/value – An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.

scholarly journals Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System

2020 ◽  
Vol 11 (1) ◽  
pp. 216
Author(s):  
Mohammed Jabreel ◽  
Najlaa Maaroof ◽  
Aida Valls ◽  
Antonio Moreno
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Sentiment Analysis ◽  
Decision Maker ◽  
Language Processing ◽  
Support System ◽  
Online Reviews ◽  
User Preferences ◽  
Data Set ◽  
The Public

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.

scholarly journals Competition as a form of promotion of the beauty salon on social media platform

2021 ◽  
Vol 10 (4) ◽  
pp. 195-199
Author(s):  
Natalia Stojanowska ◽  
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Loyalty Program ◽  
Social Media Platform ◽  
Beauty Salon ◽  
The Public ◽  
Social Media Platforms ◽  
Media Platform ◽  
Formal Requirements ◽  
A Company

There are many forms of promoting a company and its products that can be organized on social media platforms. The most popular ones are: premium sales, loyalty program, promotional lotteries, competition, and although they seem similar, they differ significantly, in particular with regard to formal requirements. The aim of the article is to discuss the outline of a competition organized in the form of a public promise of reward, indication of the requirements of popular social media platforms towards organizers of such promotional campaigns, and to draw attention to the most common mistakes. Beauty salon owners planning to organize a competition in social media should especially take care of its legal correctness, because it affects not only their legal liability, but also the public perception.

scholarly journals Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets

2020 ◽  
Vol 17 (10) ◽  
pp. 3464 ◽  
Author(s):  
Viju Raghupathi ◽  
Jie Ren ◽  
Wullianallur Raghupathi
Keyword(s):  
United States ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Ebola Virus ◽  
Herd Immunity ◽  
The United States ◽  
The Public ◽  
Time Period ◽  
Document Frequency ◽  
Measles Outbreak

Text analysis has been used by scholars to research attitudes toward vaccination and is particularly timely due to the rise of medical misinformation via social media. This study uses a sample of 9581 vaccine-related tweets in the period 1 January 2019 to 5 April 2019. The time period is of the essence because during this time, a measles outbreak was prevalent throughout the United States and a public debate was raging. Sentiment analysis is applied to the sample, clustering the data into topics using the term frequency–inverse document frequency (TF-IDF) technique. The analyses suggest that most (about 77%) of the tweets focused on the search for new/better vaccines for diseases such as the Ebola virus, human papillomavirus (HPV), and the flu. Of the remainder, about half concerned the recent measles outbreak in the United States, and about half were part of ongoing debates between supporters and opponents of vaccination against measles in particular. While these numbers currently suggest a relatively small role for vaccine misinformation, the concept of herd immunity puts that role in context. Nevertheless, going forward, health experts should consider the potential for the increasing spread of falsehoods that may get firmly entrenched in the public mind.

scholarly journals A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1894
Author(s):  
Yong Sun ◽  
Fengxiang Jin ◽  
Yan Zheng ◽  
Min Ji ◽  
Huimeng Wang
Keyword(s):  
Air Pollution ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Shandong Province ◽  
Kriging Interpolation ◽  
Social Media Data ◽  
The Public ◽  
Coastal Cities ◽  
Media Data ◽  
Monitoring Stations

Severe air pollution problems have led to a rise in the Chinese public’s concern, and it is necessary to use monitoring stations to monitor and evaluate pollutant levels. However, monitoring stations are limited, and the public is everywhere. It is also essential to understand the public’s awareness and behavioral response to air pollution. Air pollution complaint data can more directly reflect the public’s real air quality perception than social media data. Therefore, based on air pollution complaint data and sentiment analysis, we proposed a new air pollution perception index (APPI) in this paper. Firstly, we constructed the emotional dictionary for air pollution and used sentiment analysis to calculate public complaints’ emotional intensity. Secondly, we used the piecewise function to obtain the APPI based on the complaint Kernel density and complaint emotion Kriging interpolation, and we further analyzed the change of center of gravity of the APPI. Finally, we used the proposed APPI to examine the 2012 to 2017 air pollution complaint data in Shandong Province, China. The results were verified by the POI (points of interest) data and word cloud analysis. The results show that: (1) the statistical analysis and spatial distribution of air pollution complaint density and public complaint emotion intensity are not entirely consistent. The proposed APPI can more reasonably evaluate the public perception of air pollution. (2) The public perception of air pollution tends to the southwest of Shandong Province, while coastal cities are relatively weak. (3) The content of public complaints about air pollution mainly focuses on the exhaust emissions of enterprises. Moreover, the more enterprises gather in inland cities, the public perception of air pollution is stronger.

scholarly journals Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data

2021 ◽  
Vol 10 (3) ◽  
pp. 126
Author(s):  
Yong Sun ◽  
Min Ji ◽  
Fengxiang Jin ◽  
Huimeng Wang
Keyword(s):  
Air Pollution ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Analytical Framework ◽  
Shandong Province ◽  
Emotional Intensity ◽  
Air Pollution Control ◽  
Public Response ◽  
The Public ◽  
Exhaust Gas Emissions

As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for public response to air pollution based on online complaint data and sentiment analysis. In the proposed framework, the emotional dictionary of air pollution was firstly constructed using microblog data and complaint data. Secondly, the emotional dictionary of air pollution and the sentiment analysis method were used to calculate public complaints’ emotional intensity. Besides, the spatial and temporal characteristics of air pollution complaint data and public emotional intensity, the complaints content, and their correlation with PM2.5 (particulate matters smaller than 2.5 micrometers) and PM10 were analyzed using address matching, spatial analysis, and word cloud analysis. Finally, the proposed framework was applied to 13,469 air pollution complaint data in Shandong Province from 2012 to 2018. The obtained results indicated that: the public was mainly complaining about the exhaust gas emissions from enterprises and factories. Spatially, the geographical center of complaint data was located in the inland industrial urban agglomeration of Shandong Province. Correlatively, air pollution complaints’ negative emotional intensity was significantly negatively correlated with PM2.5 (−0.73). Moreover, the number of public complaints about air pollution and the intensity of negative emotions also decreased with improved air quality in Shandong Province in recent years.

scholarly journals Sentiment Analysis of Twitter for Election Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 6187-6192
Keyword(s):  
Political Parties ◽  
Sentiment Analysis ◽  
The Past ◽  
Data Mining Algorithms ◽  
News Organizations ◽  
Election Prediction ◽  
Social Media Platforms ◽  
Sentiment Score ◽  
Topic Sentiment ◽  
Mining Algorithms

Elections are considered to be the most important feature of a democracy. In the past few years, election analysis and predictions have become very important for political parties and news organizations. The influx of various social media platforms such as twitter, Facebook and YouTube have drawn a large number of people that share their ideological and political thoughts and hence, it's become important to analyse them in a much more sophisticated manner. Various data mining algorithms have been used to extract tweets and perform sentiment analysis pertaining to a related topic. Sentiment analysis refers to the technique to identify positive, negative or neutral opinions from a text. Though the use of sentiment analysis we will analyse the sentiment score for the two main political parties of India. The paper will brief on various techniques that have been used for election predictions. Various results from different methods have been included in this paper along with precision, accuracy and validity of the final outcome. The main aim of this paper is to create a model for the better prediction that will help in the analysis of voting choices of users. To increase the validity of the final results, various refining techniques have been used so that only relevant tweets are analysed.

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