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Article
Publication date: 8 February 2021

Alejandra Segura Navarrete, Claudia Martinez-Araneda, Christian Vidal-Castro and Clemente Rubio-Manzano

This paper aims to describe the process used to create an emotion lexicon enriched with the emotional intensity of words and focuses on improving the emotion analysis…

Abstract

Purpose

This paper aims to describe the process used to create an emotion lexicon enriched with the emotional intensity of words and focuses on improving the emotion analysis process in texts.

Design/methodology/approach

The process includes setting, preparation and labelling stages. In the first stage, a lexicon is selected. It must include a translation to the target language and labelling according to Plutchik’s eight emotions. The second stage starts with the validation of the translations. Then, it is expanded with the synonyms of the emotion synsets of each word. In the labelling stage, the similarity of words is calculated and displayed using WordNet similarity.

Findings

The authors’ approach shows better performance to identification of the predominant emotion for the selected corpus. The most relevant is the improvement obtained in the results of the emotion analysis in a hybrid approach compared to the results obtained in a purist approach.

Research limitations/implications

The proposed lexicon can still be enriched by incorporating elements such as emojis, idioms and colloquial expressions.

Practical implications

This work is part of a research project that aids in solving problems in a digital society, such as detecting cyberbullying, abusive language and gender violence in texts or exercising parental control. Detection of depressive states in young people and children is added.

Originality/value

This semi-automatic process can be applied to any language to generate an emotion lexicon. This resource will be available in a software tool that implements a crowdsourcing strategy allowing the intensity to be re-labelled and new words to be automatically incorporated into the lexicon.

Details

The Electronic Library , vol. 39 no. 1
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 14 May 2018

Georgios Kalamatianos, Symeon Symeonidis, Dimitrios Mallis and Avi Arampatzis

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the…

Abstract

Purpose

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the Greek language and the microblogging platform Twitter, investigating methods for extracting emotion of individual tweets as well as population emotion for different subjects (hashtags).

Design/methodology/approach

The authors propose and investigate the use of emotion lexicon-based methods as a mean of extracting emotion/sentiment information from social media. The authors compare several approaches for measuring the intensity of six emotions: anger, disgust, fear, happiness, sadness and surprise. To evaluate the effectiveness of the methods, the authors develop a benchmark dataset of tweets, manually rated by two humans.

Findings

Development of a new sentiment lexicon for use in Web applications. The authors then assess the performance of the methods with the new lexicon and find improved results.

Research limitations/implications

Automated emotion results of research seem promising and correlate to real user emotion. At this point, the authors make some interesting observations about the lexicon-based approach which lead to the need for a new, better, emotion lexicon.

Practical implications

The authors examine the variation of emotion intensity over time for selected hashtags and associate it with real-world events.

Originality/value

The originality in this research is the development of a training set of tweets, manually annotated by two independent raters. The authors “transfer” the sentiment information of these annotated tweets, in a meaningful way, to the set of words that appear in them.

Details

Journal of Systems and Information Technology, vol. 20 no. 2
Type: Research Article
ISSN: 1328-7265

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Article
Publication date: 4 October 2019

Carlos Molina Beltrán, Alejandra Andrea Segura Navarrete, Christian Vidal-Castro, Clemente Rubio-Manzano and Claudia Martínez-Araneda

This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the…

Abstract

Purpose

This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon was compared with that based on another lexicon without intensity values.

Design/methodology/approach

The authors propose a method for the automatic extraction of the affective intensity values of words using the similarity metrics implemented in WS. First, the intensity values were calculated for words having an affective root in WordNet. Then, to evaluate the effectiveness of the proposal, the results of the affective analysis based on a labelled lexicon were compared to the results of an analysis with and without affective intensity values.

Findings

The main contribution of this research is a method for the automatic extraction of the intensity values of affective words used to enrich a lexicon compared with the manual labelling process. The results obtained from the affective analysis with the new lexicon are encouraging, as they provide a better performance than those achieved using a lexicon without affective intensity values.

Research limitations/implications

Given the restrictions for calculating the similarity between two words, the lexicon labelled with intensity values is a subset of the original lexicon, which means that a large proportion of the words in the corpus are not labelled in the new lexicon.

Practical implications

The practical implications of this work include providing tools to improve the analysis of the feelings of the users of social networks. In particular, it is of interest to provide an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyberbullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

Social implications

This work is interested in providing an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyber bullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

Originality/value

The originality of the research lies in the proposed method for automatically labelling the words of an affective lexicon with intensity values by using WS. To date, a lexicon labelled with intensity values has been constructed using the opinions of experts, but that method is more expensive and requires more time than other existing methods. On the other hand, the new method developed herein is applicable to larger lexicons, requires less time and facilitates automatic updating.

Details

The Electronic Library, vol. 37 no. 6
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 28 May 2021

Xin Tian, Wu He and Feng-Kwei Wang

In recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis…

Abstract

Purpose

In recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.

Design/methodology/approach

This study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.

Findings

This research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.

Originality/value

This study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 28 May 2020

Zhan Xu

Natural disasters are increasingly more frequent and intense, which makes it critical for emergency managers to engage social media users during crises. This study…

Abstract

Purpose

Natural disasters are increasingly more frequent and intense, which makes it critical for emergency managers to engage social media users during crises. This study examined emergency official accounts' social media engagement at each disaster stage based on Fink's four-stage model of crisis and disaster: prodromal, acute, chronic and termination stages and linked topics and sentiments to engagement.

Design/methodology/approach

Using text mining and sentiment analysis, 1,226 original tweets posted by 66 major emergency official Twitter accounts and more than 15,000 retweets elicited across the life cycle of Hurricane Irma were analyzed.

Findings

Results identified the most engaging official accounts and tweets. Most tweets and the most engaging tweets were posted in the prodromal stage. Tweets related to certain topics were significantly more engaging than others. The most frequently tweeted topics by official accounts were less engaging than some seldom tweeted topics. Negative sentiment words increased the engagingness of the tweet. Sadness was the strongest predictor of tweet engagement. Tweets that contained fewer sadness words were more engaging. Fear was stronger in positively predicting tweet engagement than anger. Results also demonstrated that words for fear and anger were critical in engaging social media discussions in the prodromal stage. Words for sadness made the tweets less engaging in the chronic stage.

Originality/value

This study provided detailed instructions on how to increase the engagingness of emergency management official accounts during disasters using computational methods. Findings have practical implications for both emergency managers and crisis researchers.

Details

Online Information Review, vol. 44 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

Content available
Article
Publication date: 26 January 2018

M. Lilibeth Fuentes-Medina, Estefanía Hernández-Estárico and Sandra Morini-Marrero

The purpose of this paper is to identify the critical success factors of emblematic hotels from the perspective of the guest, by analysing the direct activities that make…

Abstract

Purpose

The purpose of this paper is to identify the critical success factors of emblematic hotels from the perspective of the guest, by analysing the direct activities that make up the value chain of these types of establishments.

Design/methodology/approach

The authors use the case study methodology to derive conclusions that contribute to the development of a theory about the success factors of emblematic hotels. The case selected is the Spanish Tourist Parador chain. The authors carried out over a period of two years a data mining analysis of the online comments posted by its guests.

Findings

The results indicate that the attributes of location and facilities are critical success factors expected a priori given the nature of the business of such establishments, based on the singular nature of the buildings. Another critical success factor is personnel, which seems to indicate that the Paradors support their business model by employing highly qualified staff, but give less attention to restaurant services or the room, according to guest perceptions.

Originality/value

The paper provides required evidence on the critical success factors of emblematic hotels adapting Porter’s value chain, for the tourism accommodation sector, through the analysis of direct value chain activities. In addition, the existing literature is broadened by taking a perspective scarcely studied, the guest perception of hotel establishments, online content posted by the user on the establishment’s website, rather than simply considering the traditional views of the experts/managers, through structures questionnaires. Besides, the results provide practical and useful implications for the managements of the emblematic hotels under study.

Details

European Journal of Management and Business Economics, vol. 27 no. 1
Type: Research Article
ISSN: 2444-8451

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Article
Publication date: 2 January 2020

Futao Zhao, Zhong Yao, Jing Luan and Hao Liu

The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse…

Abstract

Purpose

The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets.

Design/methodology/approach

This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources. Meanwhile, two different methods, Pointwise Mutual Information and Word2vec, are applied to capture word associations. Finally, an evaluation procedure is performed to validate the effectiveness of the method compared with four traditional lexicons.

Findings

The experimental results from the three real-world testing data sets show that the ensemble lexicons can significantly improve sentiment classification performance compared with the four baseline lexicons, suggesting the usefulness of leveraging knowledge derived from diverse media in domain-specific lexicon generation and corresponding sentiment analysis tasks.

Originality/value

This work appears to be the first to construct financial sentiment lexicons from over 2m posts and headlines collected from more than one language source. Furthermore, the authors believe that the data set established in this study is one of the largest corpora used for Chinese stock market lexicon acquisition. This work is valuable to extract collective sentiment from multiple media sources and provide decision-making support for stock market participants.

Details

Industrial Management & Data Systems, vol. 120 no. 3
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 26 February 2021

Shrawan Kumar Trivedi and Amrinder Singh

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition…

Abstract

Purpose

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Design/methodology/approach

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Findings

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

Research limitations/implications

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Originality/value

Twitter analysis of food-based companies has been performed.

Details

Global Knowledge, Memory and Communication, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9342

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Article
Publication date: 16 February 2021

Sonia Osorio Angel, Adriana Peña Pérez Negrón and Aurora Espinoza-Valdez

Most studies on Sentiment Analysis are performed in English. However, as the third most spoken language on the Internet, Sentiment Analysis for Spanish presents its…

Abstract

Purpose

Most studies on Sentiment Analysis are performed in English. However, as the third most spoken language on the Internet, Sentiment Analysis for Spanish presents its challenges from a semantic and syntactic point of view. This review presents a scope of the recent advances in this area.

Design/methodology/approach

A systematic literature review on Sentiment Analysis for the Spanish language was conducted on recognized databases by the research community.

Findings

Results show classification systems through three different approaches: Lexicon based, Machine Learning based and hybrid approaches. Additionally, different linguistic resources as Lexicon or corpus explicitly developed for the Spanish language were found.

Originality/value

This study provides academics and professionals, a review of advances in Sentiment Analysis for the Spanish language. Most reviews on Sentiment Analysis are for English, and other languages such as Chinese or Arabic, but no updated reviews were found for Spanish.

Details

Data Technologies and Applications, vol. 55 no. 4
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 9 September 2021

Shruti Gulati

This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper…

Abstract

Purpose

This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper reports mentioning about the government’s reaction to vaccine tourism, there is no such study or report that tries to understand what the global masses feel about it; thus, a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter is carried out.

Design/methodology/approach

This exploratory study serves as a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter and tries to categorise them into eight basic emotions from Plutchik (1994) “wheel of emotions” as joy, disgust, fear, anger, anticipation, sadness, trust and surprise. The results are presented through data visualisation technique for analysis. The study makes use of R programming languages and the extensive packages offered on RStudio.

Findings

A total of 12,258 emotions were captured. It is evident that Vaccine Tourism has got maximum of positive sentiments (28.14%) which is almost double of the negative sentiment (14.05%). It is visible that the highest sentiment is “trust” (12.74%) and is followed by “fear” (8.97%). The least visible sentiment is “surprise” (4.32%). Polarity has been found for maximum tweets as positive (55.52%) which yet again surpasses negative polarity (33.7%), and neutral polarity is the least (10.67%).

Research limitations/implications

It can be said that people bear a positive emotion regarding vaccine tourism such as “trust” and “joy” which also denotes a positive sentiment score for testing polarity. But there are still concerns of high prices of the packages, fear-prevalent people to step out, and the uncertainty of right precautionary measures being taken still puts vaccine tourism under the radar of doubt with a fourth population having negative and neutral sentiments each. This is indicative with “fear” being the second highest emotion to the users. There are mixed emotions for vaccine tourism, but positive dominates the results.

Practical implications

The study attempts to see the global reaction on social media on vaccine tourism trend for giving food for thought to marketers. It can be said that Asians can be the target group.

Originality/value

To the best of the authors’ knowledge, there is no study that addresses the new trend of “Vaccine Tourism” or attempts to understand the emotions and sentiments of people globally.

Details

Global Knowledge, Memory and Communication, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9342

Keywords

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