Natural Language Text Data Research Articles

The Flickr frequency norms: What 17 years of images tagged online tell us about lexical processing.

Word frequency is one of the best predictors of language processing. Typically, word frequency norms are entirely based on natural-language text data, thus representing what the literature typically refers to as purely linguistic experience. This study presents Flickr frequency norms as a novel word frequency measure from a domain-specific corpus inherently tied to extra-linguistic information: words used as image tags on social media. To obtain Flickr frequency measures, we exploited the photo-sharing platform Flickr Image (containing billions of photos) and extracted the number of uploaded images tagged with each of the words considered in the lexicon. Here, we systematically examine the peculiarities of Flickr frequency norms and show that Flickr frequency is a hybrid metrics, lying at the intersection between language and visual experience and with specific biases induced by being based on image-focused social media. Moreover, regression analyses indicate that Flickr frequency captures additional information beyond what is already encoded in existing norms of linguistic, sensorimotor, and affective experience. Therefore, these new norms capture aspects of language usage that are missing from traditional frequency measures: a portion of language usage capturing the interplay between language and vision, which - this study demonstrates - has its own impact on word processing. The Flickr frequency norms are openly available on the Open Science Framework(https://osf.io/2zfs3/).

Emotion Analysis of Cross-Media Writing Text in the Context of Big Data.

Since the beginning of the 21st century, sentiment analysis has been one of the most active research fields in natural language processing. Now sentiment analysis technology has not only achieved significant results in academia, but also has been widely used in practice. From business services to political campaigns, sentiment analysis is used in more and more fields. Sentiment analysis is essentially to dig out the user’s emotional attitude from the massive emotional natural language text data, and analyze the emotional dynamics of the text author through certain technical means. At present, there is almost no sentiment analysis in cross-media writing content, and it can rarely help cross-media writing vision to advance with the times and comprehensive improvement of writing ability to adapt to the current rapidly developing information society; the commonly used text media in the digital age are not. Then there is the only composition tool. Various new media appearing with the development of the ages continue to intervene in writing, and it is the general trend to cultivate media literacy in writing. The main content of this paper is the research on the emotional intensity of students’ Internet public Aiming at the shortcomings of the topic feature word selection in the sentiment tendency analysis of the students’ Internet public opinion, improvements have been made to facilitate the research on the sentiment intensity of the sentiment analysis of the students’ Internet public opinion. Sentiment analysis of students’ cross-media written text through an improved MapReduce combinator model.

Automatic Identification of Authors’ Stylistics and Gender on the Basis of the Corpus of Russian Fiction Using Extended Set-theoretic Model with Collocation Extraction

We present a novel quantitative approach for classification of authors' stylistics and gender differences based on extraction of word collocation. The proposed algorithm attenuates previously described issues of text processing using the vector models. We demonstrate the approach by analyzing a corpus of Russian prose. We discuss different approaches for classification and identification of the author's style implemented by currently-available software solutions and libraries of morphological analysis, methods of parameterization, indexing of texts, artificial intelligence algorithms and knowledge extraction. Our results demonstrate the efficiency and relative advantage of regression decision tree methods in identifying informative frequency indexes in a way that lends itself to their logical interpretation. We develop a toolkit for conducting comparative experiments to assess the effectiveness of classification of natural language text data, using vector, set-theoretic and the author's set-theoretic with collocation extraction models of text representation. Comparing the ability of different methods to identify the style and gender differences of authors of fiction works, we find that the proposed approach incorporating collocation information alleviates some of the previously identified deficiencies and yields overall improvements in the classification accuracy.

Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations

Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.

Method of automated detection of abusive content in social media text messages

The work provides the analysis of specifics of web users’ communication. Special attention is paid to the communication in social media and, in particular, to the problem of detection of abusive content in social media users’ messages. The main distinctive features of communication in social media are defined, and also the analysis of causes that can disimprove the accuracy of automated detection of abusive content is performed. The stages of processing the natural language text data are analysed, in particular the detection of abusive content, concerning the possibility of its modification for considering the peculiarities of communication in social media and other aspects that can influence the final result of text data classification. It is defined, that peculiar features of messages and special aspects of communication in social media such as the existence of symbols and numbers that can lead to confusion in message apprehension, the existence of emoji, context (at a level of social media users’ connections) and context (at a level of social media messages) can be concerned in the process of automated detection of abusive content in social media text messages. Corresponding modifications of text pre-processing stages and the analysis of classification results are defined. Method of automated detection of abusive content in social media text messages is proposed based on the principles of machine learning with modified approach to natural language text data pre-processing. Incoming data format for efficient detection of abusive content in them is proposed.

Classifying natural-language spatial relation terms with random forest algorithm

ABSTRACTThe exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.

From Texts to Networks: Detecting and Managing the Impact of Methodological Choices for Extracting Network Data from Text Data

This thesis (Diesner in Technical Report CMU-ISR-12-101, 2012) addresses a series of methodological problems related to extracting information on socio-technical networks from natural language text data. Theories and models from the social sciences are leveraged and combined with computational approaches to (a) construct, analyze and compare network data and (b) combine text data and network data for analysis. This thesis entails various projects that serve three purposes: First, the impact of various common coding choices, including reference resolution and co-occurrence-based link formation, on network data and analysis results is empirically identified across multiple types of text data and domains. Second, different relation extraction methods are compared across various over-time, open-source, large-scale datasets with respect to the resulting network data and analysis results. This study offers a complement to traditional strategies for accuracy assessment. The relation extraction methods considered include network data construction based on (a) manually versus automatically built thesauri, (b) meta-data, and (c) collaboration with subject matter experts. Third, the concepts of grouping and roles from network analysis are integrated with text mining methods to enable the theoretically grounded, joint consideration of text data and network data for real-world applications.