1,555 research outputs found

    Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

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    Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively

    Indie encounters: exploring indie music socialising in China

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    Indie music, a genre deeply rooted in rock and punk music, is renowned for its independence from major commercial record labels. It has emerged as a choice for music consumers seeking alternatives to mainstream popular music, catering to a niche music preference. The minority nature of indie music not only provides its lovers with a profound space for individual expression and a sense of collective belonging but also introduces other challenges into their social lives. Recently, the field of music sociology has proposed a more diverse perspective to observe and analyse the intricate role of music for individuals and society. In this context, regarding Chinese indie music lovers with niche music preferences, how their indie music practices integrate into their social lives and how they navigate their niche music tastes have become worthwhile topics of exploration. Drawing on interviews with 31 Chinese indie music lovers and extensive online ethnography, this thesis investigates how Chinese indie music lovers comprehend and engage with indie music, and how the power of indie music shapes them and their social behaviours. I employ the theoretical framework of ‘music in action’ (Hennion, 2001; DeNora, 2011, 2016) and symbolic interactionism (Mead, 1934; Goffman, 1959; Blumer, 1969) to examine the dynamic and multifaceted roles of indie music in the social lives of Chinese indie music lovers. I develop a concept of ‘music socialising’ to delve into several key aspects of music lovers’ social practices. I contend that through various forms of musical activities such as music selection, live music attendance, and digital practices, indie music lovers exhibit strategic and reflexive characteristics in their music practices. These practices actively contribute to constructing and maintaining self and identity, negotiating social ties, and forming and mediating collectivity within a broader social landscape. It is through these processes that the music practices of Chinese indie music lovers are endowed with meanings, thereby shaping their social reality. This thesis presents a rich and nuanced picture of the social experiences of Chinese indie music lovers, uncovering the transformative power of their indie music practices. It presents a compelling argument for the significance of music as a social agency, highlighting the complex interactions between music, individuals, and society. By bridging theoretical insights with rich empirical data, this thesis contributes to our understanding of the socio-cultural dimensions of music, offering fresh perspectives on the role of indie music in contemporary Chinese society

    Location Reference Recognition from Texts: A Survey and Comparison

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    A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

    Predicting Paid Certification in Massive Open Online Courses

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    Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Qualitative analysis of online reviews of users of hospitality services

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    Савремено друштво се све више ослања на акумулирана мишљења којa могу да пронађу на интернету. Допринос корисника на технолошким платформама омогућава олакшану интеракцију између истомишљеника заједничких интересовања, и на тај начин се олакшава процес доношења одлука. У оквиру окваквог технолошког контекста, организације у услужном сектору попут туризма и гоститељства, морају да се суоче са изазовом управљања садржајима од стране корисника. Маркетиншки стручњаци су нашли начин да искористе овакве интеракције што истиче значај имплементације нових знања у организацијама које ће помоћи у прикупљању, анализирању, тумачењу и управљању онлајн друштвеним утицајима. Предмет истраживања докторске дисертације је квалитативна анализа онлајн рецензија корисника угоститељских услуга у Србији. У поређењу са нумеричким оценама корисника, текстуалне рецензије одражавају задовољство или незадовољство корисника, али на много детаљнији начин јер садрже више информација и на тај  начин се стиче реаланији увид у стварна искуства корисника. Поред квалитативне обраде текста рецензија, идентификација врсте и значаја детерминанти задовољства и незадовољства у рецензијама корисника хотелских (у зависности од типа - градски, планински или бањски) и ресторанских услуга је један од главних задатака дисертације. За потребе истраживања прикупљене су рецензије хотела и ресторана у Србији. Коришћена је комбинација квалитативних и квантитативних метода у циљу доказивања постављених хипотеза. Од квалитативних анализа примењене су анализа фреквенције речи, анализа дужине рецензија, анализа сентимента, анализа читљивости и Латентна Дирихлеова Алокација (ЛДА). Од квантитативних метода коришћена је вишеструка регресија за утврђивање међусобних утицаја варијабли. Анализом фреквенције речи издвојене су речи које су се најчешће појављивале у рецензијама хотела и ресторана. Када су у питању хотели, у позитивним рецензијама су се појављивале речи које су се односиле на карактеристичне услуге које се пружају у одређеном типу хотела и садржале су више позитивних описних придева везаних за искуство конзумације. У негативним рецензијама хотела, без обзира на тип, чешће су се појављивали негативни описни придеви и речи које су указивале на материјалне (опипљиве) елементе хотелског производа. У позитивним рецензијама ресторана је такође присутно доста позитивних описних придева, а у негативним рецензијама је наглашен негативни аспект цене услуга у ресторану. Иако су рецензије негативне, у њима је присутно доста позитивних описних придева, што указује на то да је било аспеката услуге којима су били задовољни. Анализа дужине рецензија је показала да се у рецензијама, како хотела тако и ресторана, много више речи и реченица користи за описивање негативног искуства него позитивног. Анализа читљивости је спроведена с циљем утврђивања колико је просечно година формалног образовања неопходно за разумевање рецензија на прво читање. Резултати анализе су показали да вредности индекса читљивости варирају од веома ниског (рецензије које су разумљиве свима) до веома високог (изузетно тешке за разумевање). Просечна вредност индекса читљивости указује да читаоци морају бити завршне године средње школе за разумевање текста на прво читање. Анализом сентимента анализирана су осећања у рецензијама. Распон сентимента варира од екстремно негативних до екстремно позитивних осећања, али највећи број рецензија, како позитивних тако и негативних, садржао је неутрална и позитивна осећања. Анализирајући сентимент у рецензијама ресторана, добијени су слични резултати као и код рецензија хотела. Распон вредности сентимента варира од екстремно негативних до екстремно позитивних осећања, а са порастом оцене, расте и вредност сентимента. Овакви резултати могу указивати на то да, иако су били незадовољни, искуство корисника није праћено негативним осећањима, која су често заслужна за ширење негативних електронских препорука. Применом ЛДА издвојене су детерминанте задовољства и незадовољства услугама у хотелима (у зависности од типа хотела и категорије, као и од типа госта) и ресторанима. Полазећи од претпоставке да се детерминанте задовољства и незадовољства разликују у зависности од типа хотела, категорије и типа госта добијени су резултати који делимично потврђују ове претпоставке. Претпостављено је и да се различите детерминанте утичу на задовољство и незадовољство услугама у ресторанима, што је делимично потврђено. Применом вишеструке регресије тестирани су утицаји техничких карактеристика рецензија (поларитет, читљивост и дужина) на оцене и корисност рецензија. Добијени резултати су потврдили позитивни утицај сентимента и негативни утицај дужине рецензија на оцене корисника код хотелских рецензија, а у случају ресторана нису потврђени претпостављени утицаји. У случају утицаја техничких карактеристика рецензија хотела на корисност није утврђен значајан утицај, док је код рецензија ресторана пронађен позитиван утицај дужине и негативан утицај сентимента на корисност. Резултати добијени у овој дисертацији имају бројне теоријске и практичне импликације на угоститељску делатност. Будући да је задовољство корисника интегрални део угоститељске делатности, идентификоване детерминанте задовољства и незадовољства корисника могу угоститељима помоћи да унапреде своје пословање. На основу утврђеног утицаја техничких карактеристика рецензија на оцену и корисност, угоститељи могу да теже томе да побољшају перформансе рецензија које добијају од корисника, тако што ће, пружањем услуге врхунског квалитета, смањити негативне и дуге рецензије.Savremeno društvo se sve više oslanja na akumulirana mišljenja koja mogu da pronađu na internetu. Doprinos korisnika na tehnološkim platformama omogućava olakšanu interakciju između istomišljenika zajedničkih interesovanja, i na taj način se olakšava proces donošenja odluka. U okviru okvakvog tehnološkog konteksta, organizacije u uslužnom sektoru poput turizma i gostiteljstva, moraju da se suoče sa izazovom upravljanja sadržajima od strane korisnika. Marketinški stručnjaci su našli način da iskoriste ovakve interakcije što ističe značaj implementacije novih znanja u organizacijama koje će pomoći u prikupljanju, analiziranju, tumačenju i upravljanju onlajn društvenim uticajima. Predmet istraživanja doktorske disertacije je kvalitativna analiza onlajn recenzija korisnika ugostiteljskih usluga u Srbiji. U poređenju sa numeričkim ocenama korisnika, tekstualne recenzije odražavaju zadovoljstvo ili nezadovoljstvo korisnika, ali na mnogo detaljniji način jer sadrže više informacija i na taj  način se stiče realaniji uvid u stvarna iskustva korisnika. Pored kvalitativne obrade teksta recenzija, identifikacija vrste i značaja determinanti zadovoljstva i nezadovoljstva u recenzijama korisnika hotelskih (u zavisnosti od tipa - gradski, planinski ili banjski) i restoranskih usluga je jedan od glavnih zadataka disertacije. Za potrebe istraživanja prikupljene su recenzije hotela i restorana u Srbiji. Korišćena je kombinacija kvalitativnih i kvantitativnih metoda u cilju dokazivanja postavljenih hipoteza. Od kvalitativnih analiza primenjene su analiza frekvencije reči, analiza dužine recenzija, analiza sentimenta, analiza čitljivosti i Latentna Dirihleova Alokacija (LDA). Od kvantitativnih metoda korišćena je višestruka regresija za utvrđivanje međusobnih uticaja varijabli. Analizom frekvencije reči izdvojene su reči koje su se najčešće pojavljivale u recenzijama hotela i restorana. Kada su u pitanju hoteli, u pozitivnim recenzijama su se pojavljivale reči koje su se odnosile na karakteristične usluge koje se pružaju u određenom tipu hotela i sadržale su više pozitivnih opisnih prideva vezanih za iskustvo konzumacije. U negativnim recenzijama hotela, bez obzira na tip, češće su se pojavljivali negativni opisni pridevi i reči koje su ukazivale na materijalne (opipljive) elemente hotelskog proizvoda. U pozitivnim recenzijama restorana je takođe prisutno dosta pozitivnih opisnih prideva, a u negativnim recenzijama je naglašen negativni aspekt cene usluga u restoranu. Iako su recenzije negativne, u njima je prisutno dosta pozitivnih opisnih prideva, što ukazuje na to da je bilo aspekata usluge kojima su bili zadovoljni. Analiza dužine recenzija je pokazala da se u recenzijama, kako hotela tako i restorana, mnogo više reči i rečenica koristi za opisivanje negativnog iskustva nego pozitivnog. Analiza čitljivosti je sprovedena s ciljem utvrđivanja koliko je prosečno godina formalnog obrazovanja neophodno za razumevanje recenzija na prvo čitanje. Rezultati analize su pokazali da vrednosti indeksa čitljivosti variraju od veoma niskog (recenzije koje su razumljive svima) do veoma visokog (izuzetno teške za razumevanje). Prosečna vrednost indeksa čitljivosti ukazuje da čitaoci moraju biti završne godine srednje škole za razumevanje teksta na prvo čitanje. Analizom sentimenta analizirana su osećanja u recenzijama. Raspon sentimenta varira od ekstremno negativnih do ekstremno pozitivnih osećanja, ali najveći broj recenzija, kako pozitivnih tako i negativnih, sadržao je neutralna i pozitivna osećanja. Analizirajući sentiment u recenzijama restorana, dobijeni su slični rezultati kao i kod recenzija hotela. Raspon vrednosti sentimenta varira od ekstremno negativnih do ekstremno pozitivnih osećanja, a sa porastom ocene, raste i vrednost sentimenta. Ovakvi rezultati mogu ukazivati na to da, iako su bili nezadovoljni, iskustvo korisnika nije praćeno negativnim osećanjima, koja su često zaslužna za širenje negativnih elektronskih preporuka. Primenom LDA izdvojene su determinante zadovoljstva i nezadovoljstva uslugama u hotelima (u zavisnosti od tipa hotela i kategorije, kao i od tipa gosta) i restoranima. Polazeći od pretpostavke da se determinante zadovoljstva i nezadovoljstva razlikuju u zavisnosti od tipa hotela, kategorije i tipa gosta dobijeni su rezultati koji delimično potvrđuju ove pretpostavke. Pretpostavljeno je i da se različite determinante utiču na zadovoljstvo i nezadovoljstvo uslugama u restoranima, što je delimično potvrđeno. Primenom višestruke regresije testirani su uticaji tehničkih karakteristika recenzija (polaritet, čitljivost i dužina) na ocene i korisnost recenzija. Dobijeni rezultati su potvrdili pozitivni uticaj sentimenta i negativni uticaj dužine recenzija na ocene korisnika kod hotelskih recenzija, a u slučaju restorana nisu potvrđeni pretpostavljeni uticaji. U slučaju uticaja tehničkih karakteristika recenzija hotela na korisnost nije utvrđen značajan uticaj, dok je kod recenzija restorana pronađen pozitivan uticaj dužine i negativan uticaj sentimenta na korisnost. Rezultati dobijeni u ovoj disertaciji imaju brojne teorijske i praktične implikacije na ugostiteljsku delatnost. Budući da je zadovoljstvo korisnika integralni deo ugostiteljske delatnosti, identifikovane determinante zadovoljstva i nezadovoljstva korisnika mogu ugostiteljima pomoći da unaprede svoje poslovanje. Na osnovu utvrđenog uticaja tehničkih karakteristika recenzija na ocenu i korisnost, ugostitelji mogu da teže tome da poboljšaju performanse recenzija koje dobijaju od korisnika, tako što će, pružanjem usluge vrhunskog kvaliteta, smanjiti negativne i duge recenzije.Modern society is increasingly relying on the accumulated opinions of its peers that they can find on the Internet. The contribution of consumers on technology platforms enables easier interaction between like-minded people with common interests, and thus facilitates the decision-making process. Within this technological context, service sector organizations such as tourism and hospitality have to face the challenge of consumer-driven content management. Marketing experts have found a way to take advantage of such interactions, which emphasizes the importance of implementing new knowledge in organizations that will help collect, analyze, interpret and manage online social influences. The subject of the doctoral dissertation research is the qualitative analysis of online reviews of consumers of catering services in Serbia. Compared to numerical ratings of users, text reviews reflect customer satisfaction or dissatisfaction but in a much more detailed way because they contatin more information, and thus gain a realistic insight into real consumer experiences. Identifying the type and importance of determinants of satisfaction and dissatisfaction in consumer reviews according to hotel type (city, mountain or spa hotel) is one of the main tasks of the dissertation. For the puroposes of the research, reviews of hotels and restaurants in Serbia were collected. A combination of qualitative and quantitative methods was used in order to prove the set hypotheses. Qualitative analyzes that were applied are word frequency analysis, review length analysis, sentiment analysis, readability analysis and Latent Dirichlet Allocation (LDA). Among the quantitative methods, multiple regression was used to determine the mutual influence of variables. By analyzing the frequency of words, the words that appeared most often in reviews of hotels and restaurants were singled out. When it comes to hotels, positive reviews featured words that referred to the characteristics services provided in a certain type of hotel and contained more positive descriptive adjectives related to the experience of consumption. In negative hotel reviews, regardless of the hotel type, negative descriptive adjectives and words that indicated the material (tangible) elements of the hotel products appeared more often. In the positive reviews of restaurants, there are also a lot of positive descriptive adjectives, and in negative reviews, the negative aspect of the price of restaurant’s services is emphasized. Although the reviews are negative, there are a lot of positive descriptive adjectives in them, indicating that there were aspects of the services that they were satisfied with. The analysis of the length of reviews showed that in the reviews of both hotels and restaurants, many more words and sentences are used to describe a negative expericence than a positive one. A readability analysis was conducted to determine the average number of years of formal education necessary to understand reviews on first reading. The results of analysis showed that the values of the readability index vary form very low (reviews that are understandable to everyone) to very high (extremly difficult to understand). The average value of the readability index indicates that readers must be in their senior years of high school to understand the text on the first reading. Sentiment analysis analyzed the feelings in the reviews. The range of sentiment values varies from extremely negative to extremly positive sentiments, but the largest number of reviews, both positive and negative, contained neutral and positive sentiments. By analyzing sentiment in restaurant reviews, similar resutls were obtained as in hotel reviews. The range of sentiment values vaires from extremely negative to extremely positive sentiments, and as the rating increases, so does the value of the sentiment. Such results may indicate that, although they were dissatisfied, the user experience was not accompanied by negative feeling, which are often responsible for the spread of negative electronic recommendation. Using LDA, the determinants of satisfaction and dissatisfaction with services in hotels (depending on the type of hotel and category, as well as the type of traveler) and restaurants were isolated. Based on the assumption that the determinants of satisfaction and dissatisfaction differ depending on the type of hotel, category and type of travelers, obtained results partially confirm these assumptions. It was assumed that different determinants influence satisfaction and dissatisfaction with restaurant services, which was partially confirmed. By using multiple regression, the effects of the technical characteristics of reviews (polarity, readability and length) on the ratings and helpfulness of the reviews were tested. The obtained results confirmed the positive impact of sentiment and the negative impact of the length of reviews on user rating of hotel reviews. In the case of restaurants, the assumed impacts were not confirmed. In the case of the influence of tecnical characteristics of hotel reviews on reviews helpfulness, no significant influence was found, while in the case of restaurant reviews, a positive influence of length and a negative influence of sentiment on review helpfulness were found. The results obtained in this dissertation have numerous theoretical and practical implications for the hospitality industry. Since customer satisfaction is an integral part of the hospitality business, the identified determinants of customer satisfaction and dissatisfaction can help hoteliers and restauraters improve their business. Based on the established impact of technical characteristics of review on rating and helpfulness, hoteliers and restauraters can strive to improve the performance of reviews they receive from customers by reducing negative and long reviews by providing superior service

    A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

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    Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation

    Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

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    Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

    The Influencer Effect: Exploring the Persuasive Communication Tactics of Social Media Influencers in the Health and Wellness Industry

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    With the emergence of social media platforms such as Instagram and TikTok, social media influencers (SMIs) have been a growing source of information in the health and wellness industry. Through their creative, informative, and appealing content, SMIs have the innate ability to reach and attain a large following on social media platforms. The purpose of this study is to ascertain an understanding of the persuasive tactics employed by SMIs in the creation and dissemination of information in the health and wellness industry. Using the theoretical framework of Aristotle’s Rhetorical Appeals and Fisher’s Narrative Paradigm, this qualitative study seeks to examine the key persuasive tactics used by SMIs in the health and wellness industry. Using content analysis, the social media content of SMIs was collected and analyzed to find emerging themes related to the rhetorical appeals and narration. In addition, a comparative analysis of the persuasive tactics used by SMIs and subject-matter experts (SMEs) was conducted. Findings showed that SMIs rely heavily on the appeals that allowed them to present themselves as credible, relatable, and similar to their followers; SMEs rely strongly on the logos appeal using technical language, memes, and textual graphics to educate the audience. Through this study, using the findings of the content and comparative analysis, a list of best practices of key persuasive tactics has been established to enable SMEs to be more effective in encouraging online users to adopt health information

    The effects of twitter sentiment on renewable energy stock's returns : a Portuguese study about EDP renováveis stocks

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    Investors’ rationality in the decision-making process has been topic of discussion in the last decades due to conflicts between schools of thought. Several anomalies in the Efficient Market Hypothesis (EMH) led to a new line of thought in the matter of rationality called behavior finance. Sentiment analysis is one branch of this new school of thought who studies investors’ emotions influence on economic variables. There is no consensus between academics if these emotions can make the investment decision biased or not. The aim of this paper is to observe if the prevailing sentiment in tweets can predict the stock returns for a renewable energy company of the Portuguese market. This study looks at the second biggest company by capitalizations of the Portuguese market, EDP Renováveis (EDPR), in the period from the June 1st 2021, to June 1st 2022, and finds no significant evidence of a relationship between Twitter mood and EDP Renováveis stock returns. The reasons for this result might be explained by EDPR belonging to a very small and concentrated market, corroborating the existing theory, as well as the stakeholder composition of the company only having a very small percentage of individual investors, being this kind of investors the most influenced by biases and heuristics present in the tweets. These findings have implications for the development of the sentiment analysis theory, giving more details of the influence of sentiment in smaller and concentrated market, in the renewable energy branch, and in the period of the beginning of the war between Ukraine and Russia and the worldwide economic recovery from the Covid-19 pandemic.A racionalidade dos investidores no processo de decisão de investimento tem sido tópico de discussão nas últimas décadas devido ao conflito entre duas linhas de pensamento diferentes. Várias anomalias que não iam de encontro com a hipótese do mercado eficiente deram origem a uma nova escola de pensamento em relação à racionalidade dos investidores chamada de finanças comportamentais. Análise de sentimentos é um dos ramos desta nova linha de pensamento que estuda a influência das emoções dos investidores em diferentes variáveis económicas. Não existe consenso entre académicos se estas emoções conseguem enviesar as decisões de investimento ou não. O objetivo desta tese é observar se o sentimento presente em tweets consegue fazer prever os retornos das ações de uma empresa de energias renováveis do mercado português. Este estudo analisa a segunda maior empresa portuguesa por capitalizações, a EDP Renováveis (EDPR), no período temporal entre o dia 1 de junho de 2021 e o dia 1 de julho de 2022, e não encontrou evidência com significância de uma relação entre o estado de espírito do Twitter e os retornos das ações da EDP Renováveis. As razões que justificam estes resultados podem ser o facto da EDPR pertencer a um mercado muito pequeno e concentrado como o português, indo de encontro com a evidência empírica, assim como a composição dos proprietários das ações da empresa ter uma percentagem muito reduzida de investidores individuais, que são o tipo de investidor mais facilmente influenciado por heurísticas presentes nos tweets. Este resultado tem implicações para o desenvolvimento da teoria de análise do sentimento, dando mais detalhes da influência deste em mercados mais pequenos e concentrados, no ramo das energias Renováveis, no período de tempo do início da guerra entre a Ucrânia e a Rússia e a recuperação financeira mundial pós-Covid-19
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