Abstract The availability of personal writings in electronic format provides researchers in the fields of linguistics, psychology, and computational linguistics with an unprecedented chance to study, on a large scale, the relationship between language use and the demographic background of writers, allowing us to better understand people across different demographics. In this article, we analyze the relation between language and demographics by developing cross-demographic word models to identify words with usage bias, or words that are used in significantly different ways by speakers of different demographics. Focusing on three demographic categories, namely, location, gender, and industry, we identify words with significant usage differences in each category and investigate various approaches of encoding a word’s usage, allowing us to identify language aspects that contribute to the differences. Our word models using topic-based features achieve at least 20% improvement in accuracy over the baseline for all demographic categories, even for scenarios with classification into 15 categories, illustrating the usefulness of topic-based features in identifying word usage differences. Further, we note that for location and industry, topics extracted from immediate context are the best predictors of word usages, hinting at the importance of word meaning and its grammatical function for these demographics, while for gender, topics obtained from longer contexts are better predictors for word usage.

The subjective nature of humor makes computerized humor generation a challenging task.We propose an automatic humor generation framework for filling the blanks in Mad Libs R stories, while accounting for the demographic backgrounds of the desired audience.We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk.We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine-tune BERT to classify location-specific humor in a sentence.We leverage these components to produce YODALIB, a fully-automated Mad Libs style humor generation framework, which selects and ranks appropriate candidate words and sentences in order to generate a coherent and funny story tailored to certain demographics.Our experimental results indicate that YODALIB outperforms a previous semi-automated approach proposed for this task, while also surpassing human annotators in both qualitative and quantitative analyses.

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles' authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women.Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data.The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers.We release our data to the research community.

Abstract Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one’s most intimately held thoughts, opinions, and beliefs. This relatively new type of data is able to represent finer and more narrowly defined demographic slices. If until now researchers have primarily focused on leveraging personalized content to identify latent information such as gender, nationality, location, or age, this article seeks to establish a structured way of extracting possessions, or items that people own or are entitled to, as a way to ultimately provide insights into people’s behaviors and characteristics. We introduce the new task of ‘possession identification in text’, and release a novel dataset where possessions are marked at different confidence levels. We present experiments and results obtained when seeking to automatically identify and extract possessions from the text.

Emotions are grounded in contextual experience. While natural language processing tools typically look at textual content to find clues pertaining to an author's emotional state, factors occurring throughout the day, such as weather or news exposure, may prime one toward a particular emotional response. In this paper, we explore five types of external factors and through extensive analyses show their impact and correlation with a user's emotional state. Ultimately, we show that when combining all extrinsic features, we are able to predict with an accuracy of 67% the emotional state of a user.

Variations of word associations across different groups of people can provide insights into people's psychologies and their world views.To capture these variations, we introduce the task of demographicaware word associations.We build a new gold standard dataset consisting of word association responses for approximately 300 stimulus words, collected from more than 800 respondents of different gender (male/female) and from different locations (India/United States), and show that there are significant variations in the word associations made by these groups.We also introduce a new demographic-aware word association model based on a neural net skip-gram architecture, and show how computational methods for measuring word associations that specifically account for writer demographics can outperform generic methods that are agnostic to such information.

Subjectivity analysis focuses on the automatic extraction of private states in natural language. In this paper, we explore methods for generating subjectivity analysis resources in a new language by leveraging on the tools and resources available in English. Given a bridge between English and the selected target language (e.g., a bilingual dictionary or a parallel corpus), the methods can be used to rapidly create tools for subjectivity analysis in the new language.