The literature/science boundary in sociological articles: Using fiction to discover patterns in co-authorship, author gender, and citation rank

Sociology has been described as a ‘third culture’ between science and literature. The distinctions between different orientations in sociological writing have been studied primarily through their non-textual manifestations (publication genres or venues, methodologies used, scientometric indicators, etc.). Our knowledge of how the science–literature boundary relates to the rhetorical composition of sociological texts therefore remains limited. We mixed a bespoke corpus of Czech sociological articles with a corpus of Czech short fiction to straightforwardly account for the relationship between sociology and literature. Unsupervised classification based on the distribution of most frequent verbs yielded two categories of sociological articles. Each cluster exhibited significant association with non-textual variables. Articles less similar to literature were associated with higher rates of co-authorship, citation counts, and number of women as first authors. Both clusters also displayed clear semantic differences. The signal from literary works increased variance in the textual feature space and subsequent pseudo-experimental validation confirmed its indispensability for the discovery of the association between the rhetorical pattern of verbs usage and non-textual variables related to sociological articles.

Advertising’s Self-Reference: From Early Cinema to the Super Bowl

This chapter takes up the notion of self-reference and self-reflexivity so present in cinema, media and literature studies, if only to redefine it. Self-reference is no longer understood as a textual feature of revelation that produces knowledge about media, but as a particular mode of address: when looking at self-reference from the perspective of screen advertising and screen ads, it becomes evident that rather than displaying the medium itself, self-reference acts against reactance in that it exhibits the assumed media knowledge of the viewers and celebrates media expertise. Thus, the chapter contributes to discussions about the many notions and layers of self-reference and self-reflexivity in cinema and media studies.

Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

Twitter is a social media platform that is used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, in sentiment, there is an expression that is actually the opposite of what is mean to be, and this is called sarcasm. The existence of sarcasm in a Tweet is difficult to detect automatically by a system even by humans. In this research, we propose a weighting scheme based on inconsistency between sentimen of tweet contain in Indonesian and the usage of emoji. With the weighting scheme for the detection of sarcasm, it can be used to find out a sentiment about a event, topic, individual, group, or product's review. The proposed method is by calculating the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The experimental results of the model developed, obtained f1-score 87.5%, precision 90.5% and recall 84.8%. By using the textual features and emoji models, it can detect sarcasm in a Tweet.

Design and Analysis of a Novel Authorship Verification Framework for Hijacked Social Media Accounts Compromised by a Human

Compromising the online social network account of a genuine user, by imitating the user’s writing trait for malicious purposes, is a standard method. Then, when it happens, the fast and accurate detection of intruders is an essential step to control the damage. In other words, an efficient authorship verification model is a binary classification for the investigation of the text, whether it is written by a genuine user or not. Herein, a novel authorship verification framework for hijacked social media accounts, compromised by a human, is proposed. Significant textual features are derived from a Twitter-based dataset. They are composed of 16124 tweets with 280 characters crawled and manually annotated with the authorship information. XGBoost algorithm is then used to highlight the significance of each textual feature in the dataset. Furthermore, the ELECTRE approach is utilized for feature selection, and the rank exponent weight method is applied for feature weighting. The reduced dataset is evaluated with many classifiers, and the achieved result of the F-score is 94.4%.

The translation of reporting verbs in Italian

This paper reports on a study of reporting verbs in the Harry Potter series and their translation in Italian. It offers quantitative and qualitative perspectives on how the English verbs have been translated by two Italian translators, who worked on different books of the series. This study first analyses verb usage across the three protagonists of the series (Harry, Ron, and Hermione) in English and Italian; then, it employs Caldas-Coulthard’s (1987) taxonomy of reporting verbs and compares verb categories between source and target texts to identify tendencies in the translation of this textual feature. It finally discusses the stylistic implications of translation alterations and their potential effect on character development. As such, this paper contributes not only to the limited literature on reporting verbs in translation (especially in Italian), but it also furthers the understanding of the role of reporting verbs as a characterisation device.

Image Processing and Classification, A Method for Plant Disease Detecion

The plant disease detection is the major issue of the computer vision and machine learning. The plant disease detection has the various phases like pre-processing, segmentation, feature extraction and classification. In the existing technique support vector machine is used for the classification. The support vector machine approach has the low accuracy for the plant disease detection and also it can classify data into two classes which affect its performance. The proposed methodology is based on the region based segmentation, textual feature analysis and k-nearest neighbor method is applied for the classification. The proposed method is implemented in MATLAB and results are analyzed in terms of accuracy. The proposed technique has high accuracy and compared to existing technique.

A new approach for textual feature selection based on N-composite isolated labels

Textual Feature Selection (TFS) aims to extract relevant parts or segments from text as being the most relevant ones w.r.t. the information it expresses. The selected features are useful for automatic indexing, summarization, document categorization, knowledge discovery, so on. Regarding the huge amount of electronic textual data daily published, many challenges related to the semantic aspect as well as the processing efficiency are addressed. In this paper, we propose a new approach for TFS based on Formal Concept Analysis background. Mainly, we propose to extract textual features by exploring the regularities in a formal context where isolated points exist. We introduce the notion ofN-composite isolated points as a set ofNwords to be considered as a unique textual feature. We show that a reduced value ofN(between 1 and 3) allows extracting significant textual features compared with existing approaches even for non-completely covering an initial formal context.