Corpus Of Comments Research Articles

Youtube Comment Sentimental Analysis

The amount of textual data has grown dramatically over time, opening up new avenues for machine learning (ML) and natural language processing (NLP) study. These days, sentiment analysis of comments on YouTube is a really fascinating subject.Although there are a lot of user reviews and comments on many of these films, the low consistency and quality of the material in these comments has prevented much work from being done in terms of identifying trends from them thus far. In this research, we use machine learning techniques and algorithms to perform sentiment analysis on YouTube comments pertaining to popular themes. We show that a clear picture of how real-world events affect public sentiment can be obtained by analyzing the attitudes to identify trends, seasonality, and projections. The findings indicate a strong correlation between the sentiment trends of users and the actual occurrences linked to the corresponding keywords. This study uses a YouTube extractor to perform sentiment analysis on comments on YouTube using citation sentences.To remove the noise from the corpus of comments, various data normalization algorithms were applied to the data. We created a system using six distinct machine learning techniques, including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), to perform classifying on this data set.

A Case Study of Digital Natives Using English Buzzword cue in Vocabulary Capacity in Second Language Acquisition in China

With the increasing fragmentation of people's reading habits and the prevalence of attention distractions, internet buzzwords, which focus on capturing the essence of events and reflecting the sentiments of the masses, have emerged and gained immense popularity online. However, even though there has been a series of research on this type of buzzword in China, most of the research on buzzwords tends to be limited to exploring the words evolution and their application in a certain region, leaving a huge gap in the relationship between the use of buzzwords and users vocabulary competence. Taking the buzzword cue which is a typical Anglo-Chinese style expression, for example, the case study tries to provide a new perspective in hopes of finding out the appearance of vocabulary capacity in internet buzzwords. The proposed research will employ a mixed-methods approach, integrating semi-structured interviews and focus group discussions. The former will be used to compare different kinds of digital natives comprehension of the buzzword cue after dividing them into 3 groups by their frequency of using electronic products. Simultaneously, a corpus of comments or responses will be gathered during the focus group discussions, aiming to discern any potential correlation between vocabulary competence and the use of buzzwords.

Is it about “them”? leveraging big data research to understand anti-immigrant discourse

The paper explores the potential of big data analytics for researching anti-immigrant discourse. We emphasize contextualization as an essential element of research and follow a hybrid approach inspired by best practices of computational content analysis, combining human hermeneutic expertise with supervised machine learning to classify a corpus of comments in online news communities in Singapore over 6 months ( N = 399,225). The paper highlights how big data analytics can provide a nuanced and critical apprehension of immigrant-related discourse in large social media datasets.

NewsCom-TOX: a corpus of comments on news articles annotated for toxicity in Spanish

In this article, we present the NewsCom-TOX corpus, a new corpus manually annotated for toxicity in Spanish. NewsCom-TOX consists of 4359 comments in Spanish posted in response to 21 news articles on social media related to immigration, in order to analyse and identify messages with racial and xenophobic content. This corpus is multi-level annotated with different binary linguistic categories -stance, target, stereotype, sarcasm, mockery, insult, improper language, aggressiveness and intolerance- taking into account not only the information conveyed in each comment, but also the whole discourse thread in which the comment occurs, as well as the information conveyed in the news article, including their images. These categories allow us to identify the presence of toxicity and its intensity, that is, the level of toxicity of each comment. All this information is available for research purposes upon request. Here we describe the NewsCom-TOX corpus, the annotation tagset used, the criteria applied and the annotation process carried out, including the inter-annotator agreement tests conducted. A quantitative analysis of the results obtained is also provided. NewsCom-TOX is a linguistic resource that will be valuable for both linguistic and computational research in Spanish in NLP tasks for the detection of toxic information.

Fine-Grained Emotional Calculation of Emotional Expression in Modern Visual Communication Designs

Abstract In the context of the information age, mining for emotions has become a popular topic today, and deep learning plays an important role in the task of sentiment analysis. In this study, we propose a LE-CNN-MBiLSTM fine-grained sentiment analysis model for the task of fine-grained sentiment computation for emotional expression in visual communication design. The model combines the ERNIE model and introduces a parallel CNN and dual-channel BiLSTM structure, which first mines multiple local key features in the text with CNN, then extracts the contextual semantics with BiLSTM, and extracts the fusion features by CNN-BiLSTM. Fine-grained sentiment analysis tasks are well-served by the model in this paper, with an accuracy of 93.58% and a loss function value of 0.18, respectively. Using the model to analyze the corpus of comments on visual communication design works, positive and negative emotions dominated the samples, each accounting for 50%, and the expression of sadness was particularly prominent. The model in this paper can be applied in fine-grained sentiment computation for visual communication design and can be migrated to other natural language processing domains, thus providing a new idea for the construction of network models for text sentiment analysis.

Classifying constructive comments

We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions. We define constructive comments as high-quality comments that make a contribution to the conversation. We explain the crowd worker annotation scheme and de ne a taxonomy of subcharacteristics of constructiveness. The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept. We provide models for constructiveness trained on C3 using both feature-based and a variety of deep learning approaches and demonstrate, through domain adaptation experiments, that these models capture general rather than topic- or domain-specific characteristics of constructiveness. We also examine the role that length plays in our models, as comment length could be easily gamed if models depend heavily upon this feature. By examining the errors made by each model and their distribution by length, we show that the best performing models are effective independently of comment length. The constructiveness corpus and our experiments pave the way for a moderation tool focused on promoting comments that make a meaningful contribution, rather than only filtering out undesirable content.

Just another clickbait title: A corpus-driven investigation of negative attitudes toward science on Reddit.

The public understanding of science has produced a large body of research about general attitudes toward science. However, most studies of science attitudes have been carried out via surveys or in experimental conditions, and few make use of the growing contexts of online science communication to investigate attitudes without researcher intervention. This study adopted corpus-based discourse analysis to investigate the negative attitudes held toward science by users of the social media website Reddit, specifically the forum r/science. A large corpus of comments made to r/science was collected and mined for keywords. Analysis of keywords identified several sources of negative attitudes, such as claims that scientists can be corruptible, poor communicators, and misleading. Research methodologies were negatively evaluated on the basis of small sample sizes. Other commenters negatively evaluated social science research, especially psychology, as being pseudoscientific, and several commenters described science journalism as untrustworthy or sensationalized.

Conspiracy Theories and White Supremacy on YouTube: Exploring Affiliation and Legitimation Strategies in YouTube Comments

Misinformation and hate speech are prevalent issues in social media research, as well as the rise of far-right extremists, white supremacists, and conspiracy theorists. In response to these concerns about unethical behavior on social media, this article explores how underlying social bonds proposed by conspiracists are discursively negotiated in YouTube comments. Through close qualitative analysis of a corpus of comments about the Notre Dame Cathedral fire, a target of xenophobic and conspiratorial claims, the study identifies the range of recurrent textual personae who respond to the conspiracy theories in the videos. The analytical focus is on the values these personae express and the discursive legitimation strategies used to strengthen their claims. This article is methodologically grounded in a social semiotic approach. Seven textual personae are identified in the dataset that each realizes a particular patterning of social bonds and legitimation strategies, for example, “Educators” legitimated the authority of experts and explained why content was false, while “White Supremacists” and “Inciters” sanctioned technology and negatively evaluated particular social groups. The method employed identifies the attitudinal positions and legitimation strategies that are at the heart of the various ideologies underlying conspiracy theories. It is a step toward developing approaches for combating misinformation and hate speech that are targeted at the key values of specific communities, and avoid overgeneralizing the motivations to produce and consume conspiratorial discourse. This approach is important since arguing logical points alone, without considering the key bonds people share, is unlikely to help in combating conspiratorial discourses.

Agency and Amplification

Computational techniques offer a means to overcome the amplified complexity and resource-intensity of qualitative research on online communities. However, we lack an understanding of how these techniques are integrated by researchers in practice, and how to address concerns about researcher agency in the qualitative research process. To explore this gap, we deployed the Computational Thematic Analysis Toolkit to a team of public health researchers, and compared their analysis to a team working with traditional tools and methods. Each team independently conducted a thematic analysis of a corpus of comments from Canadian news sites to understand discourses around vaccine hesitancy. We then compared the analyses to investigate how computational techniques may have influenced their research process and outcomes. We found that the toolkit provided access to advanced computational techniques for researchers without programming expertise, facilitated their interaction and interpretation of the data, but also found that it influenced how they approached their thematic analysis.

Perfect in Lithuanian: A case study based on data from Facebook comments

The aim of this paper is to analyse the semantic values of the Lithuanian perfect construction, putting them into a perspective of grammaticalization. The paper is based entirely on the data from a 2-million-word Facebook comments corpus created ad hoc for this study. The quantitative and qualitative analysis of the semantic values of the perfect tokens extracted from the corpus reveals several previously unidentified features of this Lithuanian construction. A large proportion of structures formally corresponding to the perfect should be described as copular constructions with adjectivized participles. This formal coincidence and the ambiguity generated by it in certain cases should not be seen as accidental but rather considered a likely source of the grammaticalization of the Lithuanian perfect, as the influence of its semantic features can be seen in all the perfect’s other values. Considering it as a source, it seems that the development of the Lithuanian perfect is going in two separate, but also related directions, each of which is based on a gradual abandonment of one of the two core features of the prototypical Lithuanian perfects―the subject-oriented resultatives. In the case of the transitive resultative perfects, the orientation towards the subject is lost, while in the case of the experientials, it is the resultative meaning that is lost. Of these two values, the experientials are twice as frequent, which shows that the resultative meaning is abandoned more readily than the need to express a state or a quality of the subject. However, the experiential perfects seem to present some formal differences from all the other perfect values, namely, a significantly more frequent auxiliary usage which has so far been considered accidental.

Understanding context in computer-mediated communication

Abstract With unprecedented transformations taking place in the landscape of what to say and how we mean, interactions in the digital age take on various new forms of doing and being. To make sense of “what it is that is going on” requires an understanding of the context wherein the computer-mediated communications take place. Focusing on a burgeoning online video commenting discourse in mainland China called Danmaku (a media feature that projects viewer comments onto the video, like a ‘bullet curtain’), the present study applies the schematic construct of context of situation and its paradigmatic representation developed in Systemic Functional Linguistics to a functionally-driven discussion of Danmaku context. Drawing on a corpus of comments from 18 well-received videos on Bilibili.com (a major Danmaku site in mainland China), the study provides a fine-grained analysis that highlights emergent technological and semiotic variables in the Danmaku Mode, such as anonymity, invisibility, dynamicity, and pseudo-synchronicity. It then discusses how these variables mediate the properties of Field and Tenor and further impinge upon the experiential and interpersonal meanings made in Danmaku communication. The analysis also highlights the carnivalesque nature of Danmaku which makes it an increasingly popular social media platform in mainland China.

Discovering Community Interests Approach to Topic Model with Time Factor and Clustering Methods

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users’ interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster’s nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

Word Embeddings for Constructive Comments Classification

Word embeddings are so significant today that it is common to see their application in multiple natural language tasks. Indeed, word embeddings as the first layer of a deep learning model are widely adopted and they can also be found in multiple natural language tasks such as classification of texts and named entity recognition. The focus of this paper is the identification of constructive online comments through the use of dense vector semantics such as word embeddings. We specifically explore two approaches: learning distributed word representations and the use of a pre-trained word embedding model. We evaluate these word embedding methods on a recently created constructive comments corpus comprised of 12,000 annotated news comments, intended to improve the quality of online discussions. The obtained results show how the performance of complicated architectures like recurrent and convolutional neural networks can be matched by a language model based on learning embeddings.

Circonscrire le discours de haine numérique. Processus argumentatifs, idéologies et mémoires discursives

This paper aims to characterize the discursive processes of hate speech, direct or hidden, and to analyze how digital practices could affect them. We anchor our reflection in the framework of the role of power relations and ideologies that determine hate speech through the notions of preserved ethos and "discursive memory". In this sense, our analyses involve a critical argumentative and sociolinguistic approach to discourse. In such a perspective, circulating ideologies are to be grasped from the point of view of discursive struggles and power relations. Our analysis will be based on a corpus of comments posted in response to a publication on the Facebook page "Tous contre l'Homophobie", pleading for the right to adoption for homosexual couples.

The interplay of complexity and subjectivity in opinionated discourse

This paper brings together cutting-edge, quantitative corpus methodologies and discourse analysis to explore the relationship between text complexity and subjectivity as descriptive features of opinionated language. We are specifically interested in how text complexity and markers of subjectivity and argumentation interact in opinionated discourse. Our contributions include the marriage of quantitative approaches to text complexity with corpus linguistic methods for the study of subjectivity, in addition to large-scale analyses of evaluative discourse. As our corpus, we use the Simon Fraser Opinion and Comments Corpus (SOCC), which comprises approximately 10,000 opinion articles and the corresponding reader comments from the Canadian online newspaper The Globe and Mail, as well as a parallel corpus of hard news articles also sampled from The Globe and Mail. Methodologically, we combine conditional inference trees with the analysis of random forests, an ensemble learning technique, to investigate the interplay between text complexity and subjectivity. Text complexity is defined in terms of Kolmogorov complexity, that is, the complexity of a text is measured based on its description length. In this approach, texts which can be described more efficiently are considered to be linguistically less complex. Thus, Kolmogorov complexity is a measure of structural surface redundancy. Our take on subjectivity is inspired by research in evaluative language, stance and Appraisal and defined as the expression of evaluation and opinion in language. Drawing on a sentiment analysis lexicon and the literature on stance markers, a custom set of subjectivity and argumentation markers is created. The results show that complexity can be a powerful tool in the classification of text into different text types, and that stance adverbials serve as distinctive features of subjectivity in online news comments.

Schadenfreude: Malicious Joy in Social Media Interactions.

The paper presents a model of Schadenfreude, pleasure at another’s misfortune, resulting in a typology of cases of this emotion. Four types are singled out: Compensation, Identification, Aversion, and Injustice Schadenfreude. The typology is first tested on a corpus of 472 comments drawn from three social media, Facebook, Twitter and Instagram. Then a specific corpus of comments is collected and analyzed concerning a specific case of Injustice Schadenfreude, the posts concerning Brexit, United Kingdom leaving the European Union. From the analysis, it emerges that spatial or factual closeness does not look necessary to feel Schadenfreude. Finally, a lexicometric automatic analysis is conducted on the general corpus of Italian comments collected using several hashtags and enriched by comments about the fire of Notre Dame, showing how even complex emotions like Schadenfreude can be automatically extracted from social media.

Toxic Comments Classification using Neural Network

Humans have built broad models of expressing their thoughts via several appliances. The internet has not only become a credible method for expressing one's thoughts, but is also rapidly becoming the single largest means of doing so. In this context, one area of focus is the study of negative online behaviors of users like, toxic comments that are threat, obscenity, insults and abuse. The task of identifying and removing toxic communication from public forums is critical. The undertaking of analyzing a large corpus of comments is infeasible for human moderators. Our approach is to use Natural Language Processing (NLP) techniques to provide an efficient and accurate tool to detect online toxicity. We apply TF-IDF feature extraction technique, Neural Network models to tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk Page.

Are online news comments like face-to-face conversation?

AbstractThis article focuses on the question of whether online news comments are like face-to-face conversation or not. It is a widespread view that online comments are like “dialogue”, with comments often being referred to as “conversations”. These assumptions, however, lack empirical back-up. In order to answer this question, we systematically explore register-relevant properties of online news comments using multi-dimensional analysis (MDA) techniques. Specifically, we apply MDA to establish what online comments are like by describing their linguistic features and comparing them to traditional registers (e.g. face-to-face conversation, academic writing). Thus, we tap theSFU Opinion and Comments Corpusand the Canadian component of theInternational Corpus of English. We show that online comments are not like spontaneous conversation but rather closer to opinion articles or exams, and clearly constitute a written register. Furthermore, they should be described as instances of argumentative evaluative language.

The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments

We present the SFU Opinion and Comments Corpus (SOCC ), a collection of opinion articles and the comments posted in response to the articles. The articles include all the opinion pieces published in the Canadian newspaper The Globe and Mail in the 5-year period between 2012 and 2016, a total of 10,339 articles and 663,173 comments. SOCC is part of a project that investigates the linguistic characteristics of online comments. The corpus can be used to study a host of pragmatic phenomena. Among other aspects, researchers can explore: the connections between articles and comments; the connections of comments to each other; the types of topics discussed in comments; the nice (constructive) or mean (toxic) ways in which commenters respond to each other; how language is used to convey very specific types of evaluation; and how negation affects the interpretation of evaluative meaning in discourse. Our current focus is the study of constructiveness and evaluation in the comments. To that end, we have annotated a subset of the large corpus (1043 comments) with four layers of annotations: constructiveness, toxicity, negation and Appraisal (Martin and White, The language of evaluation, Palgrave, New York, 2005). This paper details our corpus, the data collection process, the characteristics of the corpus and describes the annotations. While our focus is comments posted in response to opinion news articles, the phenomena in this corpus are likely to be present in many commenting platforms: other news comments, comments and replies in fora such as Reddit, feedback on blogs, or YouTube comments.

“That spelling tho”: A sociolinguistic study of the nonstandard form of though in a corpus of Reddit comments

AbstractTho, the nonstandard spelling of though which was proposed by American spelling reformers in the 19th century, is making a comeback. In 2013, internet memes such as that backflip tho gave a boost to the shortened form. This sociolinguistic study investigates the use of tho in RedditGender, a 19 million-word corpus of comments posted by 1044 Reddit users. First, concordance lines generated from the whole corpus were analyzed in order to compare the use of tho with the meme and the standard spelling. Then, regression analysis was conducted with a sample of the corpus, containing only complete cases. Results show that tho is rarely used in the meme construction that contributed to popularize it, and that it appears more often as an adverb than as a conjunction. They also seem to indicate that the use of tho is correlated with gender and race. Most frequent users are black males. This suggests that the shortened spelling is not simply a way to save time when typing, and that it is not semantically equivalent to though. It seems to be a marker of affiliation with a social group and of familiarity with internet subcultures.