Snap Inc.

Luis Carlos Dos Santos Marujo

William Brendel

Leonardo Ribas Machado Das Neves

Venkata Satya Pradeep Karuturi

Jeremy Baker Voss

Ning Xu

Vitor Rocha De Carvalho

Oleksandr Grytsiuk

Mariya Apanovych

Yurii Husiev

Jianfei Yu

Robert Cornelius Murphy

Maria Pavlovskaia

Xin Chen

Wei Chu

Francesco Barbieri

Embodiments of the present disclosure relate generally to systems for presenting content suggestions within a messaging interface. A content suggestion system performs operations that include: receiving inputs that include a text string into a messaging interface; parsing a keyword from the text string of the input in response to receiving the inputs; retrieving message content that includes at least a media item based on the keyword; and presenting a chat suggestion that includes the message content within the messaging interface, according to certain example embodiments.

Content suggestion system

Sentiment classification can be implemented by an entity-level multimodal sentiment classification neural network. The neural network can include left, right, and target entity subnetworks. The neural network can further include an image network that generates representation data that is combined and weighted with data output by the left, right, and target entity subnetworks to output a sentiment classification for an entity included in a network post.

Multimodal sentiment classification

A content suggestion system to generate and cause display of a set of chat suggestions based on messages received at a client device. The content suggestions system is configured to display messages that include message content at a client device, and identify content selected by a user of the client device to be included in a response to the messages received at the client device. The content suggestion system tracks and stores a number of times in which a particular pair of content appear in succession in a chat context, and calculates a ranking of the content among a set of available content. When subsequent messages that include the content of the content pair are displayed at the client device, the content suggestion system retrieves and presents a set of content as suggestions, based on the corresponding ranks.

Systems, devices, media, and methods are presented for generating a language detection model of a language analysis system. The systems and methods access a set of messages including text elements and convert the set of messages into a set of training messages. The set of training messages are configured for training a language detection model. The systems and methods train a classifier based on the set of training messages. The classifier has a set of features representing word frequency, character frequency, and a character ratio. The systems and methods generate a language detection model based on the classifier and the set of features.

Machine learned language modeling and identification

Example embodiments described herein relate to an icon based tagging system to associate a graphical icon (e.g., an emoji) with a data category, receive media content that includes a data object and the graphical icon, assign the media content to the data category based on the presence of the graphical icon within the media content, receive a search request that comprises search criteria that includes at least the graphical icon, and present a set of search results that include the media content in response to the search request.

Icon based tagging

Symbol prediction can be implemented using a multi-task system trained for different tasks. The tasks may include a single symbol prediction, symbol category prediction, and symbol subcategory prediction. Categories of symbols can be generated by clustering sets of training data using a clustering scheme.

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Culver City, CA, United States of America

Luis Carlos Dos Santos Marujo