Context Aware Emoji Suggestions Using Emoji Embeddings and Usage Pattern

Emojis are a popular way to communicate emotions and gestures. As a type of visual art, the meaning of emojis is subject to individual perception and interpretation. The meaning and usage of emojis evolves over time. New emojis are introduced periodically. Discovering appropriate emojis can thus be difficult. This disclosure describes the use of machine learning techniques to automatically suggest emojis based on text input by a user. A machine learning model is trained over emoji annotations as well as usage patterns in a manner that adapts to changing meaning and usage of an emoji. Text entered by a user is mapped to emojis using the machine learning model. Matching emojis are ranked and provided as suggestions or autocomplete entries in the text composition interface. Emoji suggestions can be incorporated into a virtual keyboard, in a chat/messaging application, or in any other context in which emojis are used. The described techniques enable users to compose messages faster; discover and use new or trending emojis; select emojis that match the sentiment in their messages; etc

Creación de corpus de palabras embebidas de tweets generados en Argentina

El procesamiento de textos de cualquier índole es una tarea de gran interés en la comunidad científica. Una de las redes sociales donde frecuentemente las personas se expresan libremente es Twitter, y por lo tanto, es una de las principales fuentes para obtener datos textuales. Para poder realizar cualquier tipo de análisis, como primer paso se debe representar los textos de manera adecuada para que, luego, puedan ser usados por un algoritmo. En este artículo se describe la creación de un corpus de representaciones de palabras obtenidas de Twitter, utilizando Word2Vec. Si bien los conjuntos de tweets utilizados no son masivos, se consideran suficientes para dar el primer paso en la creación de un corpus. Un aporte importante de este trabajo es el entrenamiento de un modelo que captura los modismos y expresiones coloquiales de Argentina, y que incluye emojis y hashtags dentro del espacio vectorial.Text processing of any kind is a task of great interest in the scientific community. One of the social networks where people frequently express themselves freely is Twitter, and therefore, it is one of the main sources for obtaining textual data. In order to perform any type of analysis, the first step is to represent the texts in a suitable way so that they can then be used by an algorithm. This paper describes the creation of a corpus of word representations obtained from Twitter using Word2Vec. Although the sets of tweets used are not massive, they are considered sufficient to take the first step in the creation of a corpus. An important contribution of this work is the training of a model that captures the idioms and colloquial expressions of Argentina, and includes emojis and hashtags within the vector space

Emojis in Parties’ Online Communication During the 2019 European Election Campaign: Toward a Typology of Political Emoji Use

Emojis have become ubiquitous in digital communication, but we know relatively little about how they are used in political and campaigning contexts. To address this deficit, we analyze the use of emojis in the Facebook communication of parties in 11 European countries during the 2019 European election campaign. Results indicate that the use of emojis by political parties differs significantly from general online communication. Political parties more often use neutral and representational (such as flags) emojis than emotional and facial emojis to draw users’ attention while maintaining a serious appearance of their content. Based on our empirical results, we develop a typology to characterize the mixture of generic and unique functions of emojis used in political communication, outlining how they are used for (1) attracting attention, (2) visual structuring, (3) mobilizing, (4) promoting, (5), referring to political levels, (6) emphasizing policies/values, and (7) displaying affect/emotion

Moment-to-moment mood change modelling in mobile mental health network

Human interests and behaviour change over time and often affected by multiple factors. In particular, human emotions, mood and its constituent processes change and interact over time. Therefore, modelling human behaviour should take into account the changes over time for customization and adaptation of systems to the users’ specific needs. Understanding and assessing the temporal dynamics of mood are critical for modelling human behaviour for both individuals and group of people who share similar habits, life style and personal circumstances. Thus, in order to construct a personalized recommendation for a given user, it is first necessary to have some knowledge about previous user interests and behaviours. However, the challenge of obtaining large-scale data on human emotions has left the most fundamental questions on emotions less explored: How do emotions vary across individuals, evolve over time, and are connected to social ties? We address these questions using a large-scale dataset of users that contains both their users’ interactions with momentary emotions and topical labels. Using this dataset, we identify patterns of human emotions on different levels, starting from the network level, group-level (cluster) and moving towards the user level. At the user-level, we identify how human emotions are distributed and vary over time. In particular, we model changes in mood using multi-level multimodal features including users’ sentimental status, engagement and linguistic queries. We also utilise language models to model and understand patterns of mood change. We model the changes of users’ mental states based on replies and responses to posts over time and predict future states. We find that the future mental states can be predicted with reasonable accuracy given users’ historical posts, current participation features. Our findings form a step forward towards better understand the interplay between user behaviour and mood change exhibited while interacting on mental health network and providing some interpretable summaries that can be used in the future by health experts and individuals and work on possible medical interventions together with clinical experts

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202