12,003 research outputs found

    Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

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    Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

    A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.'s case

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    In this article, we show the results of a quantitative and qualitative analysis of open citations on a popular and highly cited retracted paper: “Ileal-lymphoid-nodular hyperplasia, non-specific colitis and pervasive developmental disorder in children” by Wakefield et al., published in 1998. The main purpose of our study is to understand the behavior of the publications citing one retracted article and the characteristics of the citations the retracted article accumulated over time. Our analysis is based on a methodology which illustrates how we gathered the data, extracted the topics of the citing articles and visualized the results. The data and services used are all open and free to foster the reproducibility of the analysis. The outcomes concerned the analysis of the entities citing Wakefield et al.’s article and their related in-text citations. We observed a constant increasing number of citations in the last 20 years, accompanied with a constant increment in the percentage of those acknowledging its retraction. Citing articles have started either discussing or dealing with the retraction of Wakefield et al.’s article even before its full retraction happened in 2010. Articles in the social sciences domain citing the Wakefield et al.’s one were among those that have mostly discussed its retraction. In addition, when observing the in-text citations, we noticed that a large number of the citations received by Wakefield et al.’s article has focused on general discussions without recalling strictly medical details, especially after the full retraction. Medical studies did not hesitate in acknowledging the retraction of the Wakefield et al.’s article and often provided strong negative statements on it

    Role of Machine Learning in Sentiment Analysis- A Review

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    Amongst the most basic activities in natural language processing is to know and understand low-dimensional vector presentations of words from a huge dataset. The organizational forms embedding system trains word vectors primarily from grammatical rules and semantic relations from the sense, disregarding sentiment polarity in the sentences. While some methods prototype sentiment data from feedback, they ignore specific language in various contexts. If the responded vector is easily adapted to the evaluation of sentiment classification task when the sentimentality keeps changing, the sentiment classification performance will suffer immensely. The methodologies was using to categories sentiment classification are discussed in this paper

    NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

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    This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

    Pre-research Study based on Bibliometrics, Deep Learning Research for Aspect-Based Sentiment Analysis

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     Background: Massive publications on deep learning research for aspect-based sentiment analysis are challenging for interested researchers who want to research this area. Purpose: to provide an overview and comprehensive analysis on the research trend, which include the growth of publications, the most used keywords, the most popular publication sources to publish and find literature, the most cited publication, the most productive researcher, the most productive institution and country affiliation. Method: This study used a bibliometric method to analyze Scopus's indexed publications from 2014 (the year when the first publication was first indexed) to 2020. A total of 222 publications were analyzed and visualized using the VosViewer software. Result: In general, there is an increase in the number of publications from year to year. Keyword visualization shows keywords related to text-based processing, deep learning architectures, the research object and media, and the application of the method. The most popular sources to publish and to find literature are the “Lecture Notes in Computer Science” and the “Expert Systems with Applications''. The most cited publication is “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review”, written by Do, Prasad (cited 81 times). The most productive researcher is Zhang Y from China. The most productive institution is Nanyang Technological University (6 publications), and China has the highest number of publications (76 documents). Conclusion: The bibliometric method can provide a conclusive and comprehensive preliminary overview of research trends for interested researchers who want to start research about deep learning for aspect-based sentiment analysis.   Keywords: Bibliometrics; Deep learning; Aspect-based sentiment analysis; VosViewer    Abstrak  Latar Balakang: Banyaknya publikasi mengenai penelitian deep learning untuk aspect-based sentiment analysis menjadi tantangan tersendiri bagi peneliti yang tertarik dan ingin memulai penelitian terkait topik ini. Tujuan: memberikan gambaran umum serta analisis komprehensif tren penelitian meliputi pertumbuhan jumlah publikasi, kata kunci yang banyak digunakan, sumber publikasi populer yang dapat dimanfaatkan untuk tujuan publikasi maupun menemukan literatur, publikasi utama yang paling banyak disitir, peneliti paling produktif dan pola kolaborasi peneliti, serta afiliasi institusi dan negara paling produktif. Metode: Kajian ini menggunakan metode bibliometrik untuk menganalisis publikasi terindeks Scopus dari tahun 2014 (tahun pertama kali publikasi terindeks) hingga tahun 2020. Sebanyak 222 judul publikasi dianalisis, kemudian divisualisasikan dengan software VosViewer. Hasil: Secara umum jumlah publikasi mengalami peningkatan dari tahun ke tahun. Visualisasi kata kunci menggambarkan kata kunci yang berkaitan dengan pemrosesan berbasis teks, arsitektur deep learning, obyek dan media penelitian, serta aplikasi aspect-based sentiment analysis dengan metode deep learning. Sumber publikasi terpopuler untuk tujuan publikasi dan sumber literatur utama berturut-turut adalah Lecture notes in Computer Science dan Expert Systems with Applications. Publikasi yang paling banyak disitir adalah Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review oleh Do, Prasad (disitir 81 kali). Peneliti paling produktif adalah Zhang Y dari Cina. Institusi yang paling produktif adalah Nanyang Technological University (6 publikasi), dan Cina menjadi negara paling produktif dengan jumlah publikasi sebanyak 76 dokumen. Kesimpulan: Kajian menggunakan metode bibliometrik dapat memberikan gambaran awal tren penelitian yang konklusif dan komprehensif bagi peneliti yang tertarik dan ingin memulai penelitian tentang topik deep learning untuk aspect-based sentiment analysis.   Kata kunci: Bibliometrika; Deep learning; Aspect-based sentiment analysis; VosViewer&nbsp

    Hidden Citations Obscure True Impact in Science

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    References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

    The ACL OCL Corpus: advancing Open science in Computational Linguistics

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    We present a scholarly corpus from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain, named as ACL OCL. Compared with previous ARC and AAN versions, ACL OCL includes structured full-texts with logical sections, references to figures, and links to a large knowledge resource (semantic scholar). ACL OCL contains 74k scientific papers, together with 210k figures extracted up to September 2022. To observe the development in the computational linguistics domain, we detect the topics of all OCL papers with a supervised neural model. We observe ''Syntax: Tagging, Chunking and Parsing'' topic is significantly shrinking and ''Natural Language Generation'' is resurging. Our dataset is open and available to download from HuggingFace in https://huggingface.co/datasets/ACL-OCL/ACL-OCL-Corpus
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