Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Abstract: A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives, based on installation of a content application on a client device, credentials of a user of the client device, and information identifying a social media application granted access to content associated with the user. The device receives the content from the client device. The device generates a link for the content, and stores the credentials, the content, the link, and the information identifying the social media application. The device receives information indicating that the content is be provided in a post associated with the social media application, and provides the link to the post. The device receives a selection of the link from another client device associated with another user that is viewing the post via the social media application, and provides the content, in a secure format, to the other client device based on the selection of the link.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives credentials of a user of a client device that is accessing an application associated with a server device, and verifies the user based on the credentials of the user. The device retrieves, based on verifying the user, a random function from a random functions data structure associated with the device, and utilizes the random function to select parameters and values for a user function. The device utilizes the user function, the parameters, and the values to generate a custom encryption function, and encrypts the custom encryption function to form an encrypted custom encryption function. The device provides the encrypted custom encryption function to the client device and the server device to permit encrypted communication between the client device and the server device using the custom encryption function.

Abstract: A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Abstract: A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Abstract: A device receives credentials of a user of a client device that is accessing an application associated with a server device, and verifies the user based on the credentials of the user. The device retrieves, based on verifying the user, a random function from a random functions data structure associated with the device, and utilizes the random function to select parameters and values for a user function. The device utilizes the user function, the parameters, and the values to generate a custom encryption function, and encrypts the custom encryption function to form an encrypted custom encryption function. The device provides the encrypted custom encryption function to the client device and the server device to permit encrypted communication between the client device and the server device using the custom encryption function.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives, based on installation of a content application on a client device, credentials of a user of the client device, and information identifying a social media application granted access to content associated with the user. The device receives the content from the client device. The device generates a link for the content, and stores the credentials, the content, the link, and the information identifying the social media application. The device receives information indicating that the content is be provided in a post associated with the social media application, and provides the link to the post. The device receives a selection of the link from another client device associated with another user that is viewing the post via the social media application, and provides the content, in a secure format, to the other client device based on the selection of the link.

Abstract: A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A device receives credentials of a user of a client device that is accessing an application associated with a server device, and verifies the user based on the credentials of the user. The device retrieves, based on verifying the user, a random function from a random functions data structure associated with the device, and utilizes the random function to select parameters and values for a user function. The device utilizes the user function, the parameters, and the values to generate a custom encryption function, and encrypts the custom encryption function to form an encrypted custom encryption function. The device provides the encrypted custom encryption function to the client device and the server device to permit encrypted communication between the client device and the server device using the custom encryption function.

Abstract: A device receives, based on installation of a content application on a client device, credentials of a user of the client device, and information identifying a social media application granted access to content associated with the user. The device receives the content from the client device. The device generates a link for the content, and stores the credentials, the content, the link, and the information identifying the social media application. The device receives information indicating that the content is be provided in a post associated with the social media application, and provides the link to the post. The device receives a selection of the link from another client device associated with another user that is viewing the post via the social media application, and provides the content, in a secure format, to the other client device based on the selection of the link.