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Tourism Management Through Natural Language Processing and Sentiment Analysis: A Case Study of the Main Natural Areas of Extremadura, Spain
Abstract
Nowadays, digital scenarios and global connectivity have changed the way we travel; visitors can comment and share their experiences from different sources of information, and this action can be of essential value in tourism research. Consequently, these factors can help design new marketing, management, or business strategies and make tourist destinations more competitive. Multiple techniques can process big data and machine learning analysis, such as natural language processing (NLP), which can support tourist resources in analyzing this information. This paper proposes an NLP technique to present an emotion detection analysis based on social web reviews about the main natural spaces in Extremadura, Spain. Sentiment and emotions scores have been computed based on the Plutchik model using a lexicon database to understand visitor experience. Empirical evidence suggests some recommendations regarding tourism management to improve the visitor experience and reduce negative sentiments and emotions.
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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.
The social networks and the rapid development of new technologies have led to considerable changes in the tourism industry. Artificial intelligence, in particular natural language process ing (NLP), presupposes a significant advantage in obtaining information on the mass content
generated by online users concerning tourism services and products. This work presents a systematic review of the use of NLP in the tourism industry and research. We used the well-known PRISMA methodology, and 227 relevant studies over the last decade have been reviewed. Our analysis identified the main methodologies, tools, data sources, and other relevant features in the field. One of the principal contributions of this study is a taxonomy for using NLP in tourism. In addition, metadata were examined using a three-fold approach: (i) general statistics, (ii) abstract text analysis, and (iii) keyword networks. Automatic analyses have identified six major topics in applying NLP to tourism issues and have shown that China, the United States, Thailand, and Spain share similar tourism issues or approaches.
The present empirical study aims to provide a new conceptual framework for the regional tourism industry of the Province of Tungurahua-Ecuador that emerged from a combined application of stakeholder engagement and research-practice approaches. These preliminary outcomes from in-depth focus group assessments and surveys applied to tourism experts, industry practitioners, government’s representatives, and tourists revealed that a synergic/collaborative work across tourism stakeholders have the potentials to create innovative touristic products and services, thus solving progressively the existing issues observed in the regional tourism, travel and hospitality sectors. Resulting tailor-made tourism products, mainly based on advanced technology, may represent the first steps of improvements to be taken into consideration prior to evolving to the development and implementation of smart cities and smart tourism concepts in this region where tourism activities are one of the most important revenue for the national and regional tourism industry.
Mass tourism is growing uncontrolled in many destinations worldwide. Negative impacts emerge from it, transforming the city and influencing on locals’ lifestyle and well-being. However, debates and contradictions emerge on the real impact on communities. From that, a deeper analysis of this issue is needed to understand how people perceive tourism and its effects. A quantitative study was conducted in two mature destinations in Spain:
Barcelona and Sevilla. One of the major findings from this research is the importance of locals’ support to manage tourism. Practical and managerial implications are suggested in order to promote more sustainable and participative development of destinations
This paper presents the framework and results from the Rest-Mex track at IberLEF 2021. This track considered two tasks: Recommendation System and Sentiment Analysis, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when recommending a destination of Nayarit, Mexico, based on places visited by the tourists and their opinions. On the other hand, the Sentiment Analysis task predicts the polarity of an opinion issued by a tourist who traveled to the most representative places in Guanajuato, Mexico. For both tasks, we have built new corpora considering Spanish opinions from the TripAdvisor website. This paper compares and discusses the results of the participants for both tasks.
This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.
Featured Application
The approach rapidly extracts reporting requirements from construction contracts and predicts overhead costs and durations associated with report preparation. Application of the approach is anticipated to provide the insights necessary to enhance contract negotiations, reporting workflow processes, and submittal procedures between clients and contractors.
Abstract
Due to a lack of suitable methods, extraction of reporting requirements from lengthy construction contracts is often completed manually. Because of this, the time and costs associated with completing reporting requirements are often informally approximated, resulting in underestimations. Without a clear understanding of requirements, contractors are prevented from implementing improvements to reporting workflows prior to project execution. This study developed an automated reporting requirement identification and time–cost prediction framework to overcome this challenge. Reporting requirements are extracted using Natural Language Processing (NLP) and Machine Learning (ML), and stochastic simulations are used to predict overhead costs and durations associated with report preparation. Functionality and validity of the framework were demonstrated using real contracts, and an accuracy of over 95% was observed. This framework provides a tool to rapidly and efficiently retrieve requirements and quantify the time and costs associated with reporting, in turn providing necessary insights to streamline reporting workflows.
The Spanish region of Extremadura represents a paradigmatic case within the European Union because above 30% of its territory (41,634 km²) is protected by international, national or regional figures of protection. In addition, many of these protected areas are connected between them through the so-called Armorican Arch of Cáceres which crosses the region from SE to NW as if it were a natural corridor of quartzite ridges. The main goal of this study was to identify tourist resources in this natural corridor aimed at the design of tourist routes that can suppose an opportunity of touristic development in this territory characterized by a low population density. For doing that, we have followed these methodological steps: [1] geographical delimitation of the Armorican Arch of Cáceres, [2] socio-economic analysis of the municipalities and [3] identification of the natural protected sites crossed by this corridor, [4] compilation of information on fauna and flora as well as geological and cultural aspects, and [5] design of tourist routes. This corridor passes by 52 municipalities, 7 counties, 3 provinces and 2 countries and connects the La Siberia Biosphere Reserve with the Naturtejo Geopark in Portugal passing by the Villuercas, Ibores, Jara Geopark, the Monfragüe National Park (also Biosphere Reserve), the Canchos de Ramiro y Ladronera Special Protection Area for Birds and the International Tagus River Biosphere Reserve. We have identified 81 remarkable tourist resources: 37 natural protected sites, 30 sites of geological interest and 14 medieval castles as well as the presence of fossils and valuable species of flora and fauna (some of them are endangered). By using this information we have designed three thematic tourist routes: birdwatching, medieval castles and geological sites.
Las nuevas necesidades de los turistas apuntan hacia la búsqueda de experiencias, por lo que los destinos turísticos deben focalizar sus estrategias de marketing en satisfacer estas nuevas exigencias de la demanda. Así, la innovación es una forma en que los destinos pueden mejorar sus estrategias de marketing centrándose en la oferta de experiencias, puesto que las estrategias de marketing tradicionales, enfocadas en la venta de productos o servicios, han perdido eficacia. De esta forma, el objetivo de este estudio es analizar la aplicación del enfoque del marketing expe- riencial a las innovaciones de marketing de un destino turístico, principalmente en el diseño del pro- ducto y la promoción. Con esta finalidad, se realizó una revisión de la literatura sobre innovación de marketing, con base en la cual se estableció una serie de pautas para introducir innovaciones en el diseño del producto y en la promoción turística de un destino basadas en el enfoque del marketing experiencial. Por último, a partir de estas directrices, se propusieron recomendaciones para mejorar las estrategias de marketing de Ecuador como destino turístico. Los resultados indican que los cambios en el diseño del producto basados en este enfoque buscan crear experiencias únicas y memorables, mientras que las mejoras en los medios de promoción pretenden comunicar sensaciones y emociones que un turista puede experimentar durante su visita.
Conservation of the environment has become a key factor in tourist development, as is shown by the increase in visitors to natural parks and other places with rich ecosystems. Protected areas have become polarised centers for tourists of very varied characteriztics, not only for those who make them their main destination, but also for those who travel to nearby areas. This situation can be observed in Extremadura, where numerous tourists make the best use of their stay in the main centers of tourist attractions of the region to make local trips and thus get to know the most significant natural areas. These movements are being detected in varied aspects of demand, sometimes with little connection between the main and secondary motivation for the trip. We therefore consider a variable percentage of tourists visiting the main cultural destinations of Extremadura who use part of their stay to get to know protected areas. With the aim of structuring the research, we made a systematic study of tourists who spent the night in the cities of Cáceres and Mérida, which are World Heritage sites. During their stay, they visited prestigious natural places such as Monfragüe National Park, the Villuercas-Ibores-Jara Geopark, the Tajo Internacional Nature Reserve, and the Garganta de los Infiernos Nature Reserve. This information was obtained by carrying out surveys, which allowed us to determine the attraction capacity of each protected area by applying a network analysis. The results reflect a heterogeneous type of visitor who travels to the most appreciated areas following guidelines marked by the time of the year, his/her preferences, and the duration of his/her trip.
Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.
The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, by means of a geographical information system (GIS) and spatial statistics, that it is possible to better define the groupings of rural accommodation available in Extremadura, Spain, especially if these are conceptualized by dint of their lodging capacity. To do so, two specific techniques have been used: hotspot analysis and outlier analysis, which yield results that prove the existence of homogeneous and heterogeneous groups of accommodation businesses, based not only on their spatial proximity but also on their lodging capacity. On the basis of this analysis, the regional administration can devise tourist policies and strategic plans in order to improve the management and efficiency of each business. Despite the applicability of the present results, this study also addresses the difficulties in using these techniques—Where establishing the spatial relationships and the boundary distance are key concepts. In the case study here, the ideal configuration utilizes a fixed distance of six miles.
The use of natural protected areas has been analyzed abundantly in the relevant literature, although on many occasions these areas are studied from the viewpoint of their role as a tourist destination in themselves, while neglecting their role as a tourist attraction that can be visited from the main destination of their stay. In certain specific areas, as in the case of Extremadura, protected areas are often a complementary destination for visitors who are staying in popular tourist hubs. This study is based on data from 4 different spaces (with different degrees in their status as protected areas) about the flow of tourists they generate at the destination. In light of the data, this paper identifies the 41 towns and villages with the most tourists, later to determine their mobility towards natural protected areas. Information was collected from almost 14,000 surveys that were filled at 52 tourist offices. This information allowed us to map the flow of tourists from their places of stay to the protected areas analyzed here, which resulted in the mapping of relevant networks by means of a Geographic Information System following the criterion of shortest path available. The results here clearly demonstrate that each area has a varying capacity to attract visitors, although in a large proportion of cases, the 60-min isochrone is a boundary beyond which the number of visitors drops significantly.
Artificial intelligence (AI) technologies have a great potential to aid not only in promoting tourism products and services, but also in influencing responsible travel behaviour to support sustainability. The effectiveness of using AI for positive behaviour change interventions depends on consumers’ attitudes toward AI. This study found three underlying views of AI impacts: Beneficial AI, Destructive AI, and Risky AI. Based on these, three consumer segments were identified: The Laggards, The Aficionados, and The Realists. The first two segments hold opposing views: the former averaging higher in negative impacts, while the latter in positive impacts of AI. The Realists are aware of both benefits and risks of AI. These segments differ in their intention to follow recommendations from AI. It is suggested that mainstream consumers, those belonging to The Realists, are likely to respond positively to AI systems recommending responsible behaviour, signifying the positive role of AI in sustainable tourism.
Purpose
This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.
Design/methodology/approach
The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.
Findings
Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.
Research limitations/implications
This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.
Originality/value
This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.
La comunicación de los destinos turísticos y sus marcas a través de los medios sociales recoge las conclusiones del Proyecto de I+D+I del Ministerio de Economía y Competitividad, “Uso e influencia de los social media y la comunicación 2.0 en la toma de decisiones turísticas y en la imagen de marca de los destinos. Aplicaciones de utilidad para los destinos turísticos españoles”. Igualmente se incluyen algunas recomendaciones sobre la estrategia y el uso de los medios sociales para destinos turísticos.
Social Media and consumer-generated content continue to grow and impact the hospitality domain. Consumers write online reviews to indicate their level of satisfaction with a hotel and inform other consumers on the Internet of their hotel stay experience. A number of websites specialized in tourism and hospitality have flourished on the Web (e.g. Tripadvisor). The tremendous growth of these data-generating sources demands new tools to deal with them. To cope with big amounts of customer-generated reviews and comments, Natural Language Processing (NLP) tools have become necessary to automatically process and manage textual customer reviews (e.g. to perform Sentiment Analysis). This work describes OpeNER, a NLP platform applied to the hospitality domain to automatically process customer-generated textual content and obtain valuable information from it. The presented platform consists of a set of Open Source and free NLP tools to analyse text based on a modular architecture to ease its modification and extension. The training and evaluation has been performed using a set of manually annotated hotel reviews gathered from websites like Zoover and HolidayCheck.
Se ofrecen recomendaciones para mejorar el modo en que los destinos turisticos emplean las redes sociales en su comunicacion. Mediante herramientas de medicion online se analizan las cuentas de Facebook y Twitter de 5 destinos turisticos espanoles. Se comprueba que los destinos descuidan en las redes la construccion de la identidad de marca-lugar y que el potencial interactivo se desaprovecha en gran medida, sobre todo en Facebook . Se recomienda potenciar el engagement (compromiso, fidelidad) realizando preguntas a los usuarios y utilizando fotografias, asi como incidir en la comunicacion de marca y no solo en la difusion de informacion turistica.
Many travel and tourism operations have recognized the importance of including consumer generated content on their Web sites but usually do so in the form of edited testimonials. At the same time, travelers are provided with an ever increasing number of opportunities to voice their opinions in raw/unedited and honest form. Blogs, podcasts, wikis and personal Web spaces hosted on social networking sites such as MySpace.com are growing fast in use and popularity among travelers who want to document and/or share travel experiences. Given the growing use of the Internet for travel planning, an ever greater number of travelers will tap into this "collective travel intelligence" available on the Web. This will challenge established travel guide, travel agent, as well as travel supplier Web sites (Rand, 2006).
While marketing scholars have emphasized the importance of customer satisfaction, few studies have examined in detail consumers' responses to dissatisfaction. This study examines correlates of one possible response-telling others about the dissatisfaction-and identifies variables that distinguish this response from others. Variables investigated include the nature of the dissatisfaction, perceptions of blame for the dissatisfaction, and perceptions of retailer responsiveness. Marketing management and consumer behavior research implications are discussed.
The use of the WWW as a venue for voicing opinions, complaints and recommendations on products and firms has been widely reported in the popular media. However little is known how consumers use these reviews and if they subsequently have any influence on evaluations and purchase intentions of products and retailers. This study examines the effect of negative reviews on retailer evaluation and patronage intention given that the consumer has already made a product/brand decision. Our results indicate that the extent of WOM search depends on the consumer's reasons for choosing an online retailer. Further the influence of negative WOM information on perceived reliability and purchase intentions is determined largely by familiarity with the retailer and differs based on whether the retailer is a pure-Internet or clicks-and-mortar firm. Managerial implications for positioning strategies to minimize the effect of negative word-of-mouth have been discussed.
Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.
This research's main objective is to analyse Online Travel Reviews (OTRs) related to tourist attractions aiming at identifying recurring discussion topics/themes to infer whether travellers make reference to positive or negative experiences during their trips. This study focuses on the case of Guanajuato, a Mexican cultural destination. It is important to mention that similar analyses have not been carried out to study any Mexican destination so far. OTRs are analysed using two Natural Language Processing approaches: Mutual Information Ranking and Jaccard Coefficient. These are applied to quantify and extract the most representative themes and the main topics from each polarity within the OTRs. These techniques have been extensively studied in various areas, however, to the best of our knowledge, they have not been applied in digital tourism to detect essential issues during the travel experience. As a result, two recurrent negative themes/topics (‘cleanliness’, ‘prices’) were identified throughout this analysis. One surprising finding of this work relates to the lack of variation between national and international travellers' evaluations. The findings of this study contribute to confirming the role of OTRs within the contemporary tourist experience by identifying those places perceived negatively or positively directy from the visitors' perspective.
The online sales of tourism activities are increasing despite once-sluggish growth, especially in contrast to other tourism segments, i.e., transportation and accommodation, which already had a more significant share of online sales. This article aims to provide a narrative as to why tourism activities took longer than other travel segments to be sold online and why the trend of being sold increasingly online will continue into the future. The article concludes that two key factors, the nature of tourism activities (i.e., subjective and customizable) and technological advancements, have contributed to this trend. These technological advancements include tourism activity booking software-as-a-service (SaaS), innovation in payments technology, application programming interfaces (APIs), big data, and dynamic packaging. Additionally, the COVID-19 pandemic further accelerated this trend.
Online travel reviews have become increasingly important as a result of the intangible and heterogeneous characteristics of the tourism industry as well as the recent diffusion of social media. This exploratory case study intends to examine the comparative importance of the six heuristic attributes (reviewer location, reviewer level, reviewer helpful vote, review rating, review length, and review photo) with respect to review helpfulness in the online hotel review context. Moreover, the salience of the individual levels within each review attribute and the importance of the attributes in positive and negative review-rating groups are compared. In total, 1,158 reviews of a hotel on TripAdvisor were collected and analyzed through conjoint analyses. This study found that the review rating and reviewer helpful vote attributes are the two most important factors. Finally, three major propositions are suggested based on findings of the study, and several theoretical and managerial implications are discussed.
The general psychoevolutionary theory of emotion that is presented here has a number of important characteristics. First, it provides a broad evolutionary foundation for conceptualizing the domain of emotion as seen in animals and humans. Second, it provides a structural model which describes the interrelations among emotions. Third, it has demonstrated both theoretical and empirical relations among a number of derivative domains including personality traits, diagnoses, and ego defenses. Fourth, it has provided a theoretical rationale for the construction of tests and scales for the measurement of key dimensions within these various domains. Fifth, it has stimulated a good deal of empirical research using these tools and concepts. Finally, the theory provides useful insights into the relationships among emotions, adaptations, and evolution.
Discusses research on facial expressions of emotion and presents suggestions for recognizing and interpreting various expressions. Using many photographs of faces that reflect surprise, fear, disgust, anger, happiness, and sadness, methods of correctly identifying these basic emotions and of understanding when people try to mask or simulate them are outlined. Practice exercises are also included. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The circumplex model [of personality and emotion] focuses on determining how traits and emotions are structurally similiar, and its underlying assumption is that a relatively seamless circular ordering, or circumplex, is an economical description of the relations among traits and emotions.
The circumplex model allows a broader view of personality and of the interpersonal relationships integral to understanding just what makes a personality the way it is. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Visitor experiences in natural landscapes are considered integral components of visitor satisfaction research. Many studies have considered satisfaction as a cognitive process leading to an emotional state (J. Travel Res. 34(1) (1995) 11; Outdoor Recreation Benchmark 1988. Proceedings of the National Outdoor Recreation Forum, January, Tampa FL, pp. 422–438). Such emotional states are multidimensional and multisensory manifestations that are exhibited in numerous forms. At the same time they are influenced by preferences, values, beliefs and attitudes or in other words, social cognition of a visitor. Numerous techniques have been used to collect data showing the state of emotions/experiences. Techniques range from simple questionnaires administered in situ, to maintaining diaries of individual activities and experiences, to experiential sampling methods such as those adopted by Hull and Stewart (Environ. Behav. 27 (1995) 404). Recently, more expensive but effective instruments have been used such as video (Monitoring and Management of Visitor Flows in Recreational and Protected Areas. Conference Proceedings, pp. 296–301) and sensors using counters (Monitoring and Management of Visitor Flows in Recreational and Protected Areas. Conference Proceedings, pp. 258–263).
Emotions are viewed as having evolved through their adaptive value in dealing with fundamental life-tasks. Each emotion has unique features: signal, physiology, and antecedent events. Each emotion also has characteristics in common with other emotions: rapid onset, short duration, unbidden occurrence, automatic appraisal, and coherence among responses. These shared and unique characteristics are the product of our evolution, and distinguish emotions from other affective phenomena.
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