The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. No prior work related to social media mining has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper aims to address this research gap and makes two scientific contributions to this field. First, it presents a multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. The dataset, available at https://dx.doi.org/10.21227/7fvc-y093, contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were performed. This process included classifying each post into (i) one of the sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral, (ii) hate or not hate, and (iii) anxiety/stress detected or no anxiety/stress detected. These results are presented as separate attributes in the dataset. Second, this paper presents the results of performing sentiment analysis, hate speech analysis, and anxiety or stress analysis. The variation of the sentiment classes - fear, surprise, joy, sadness, anger, disgust, and neutral were observed to be 27.95%, 2.57%, 8.69%, 5.94%, 2.69%, 1.53%, and 50.64%, respectively. In terms of hate speech detection, 95.75% of the posts did not contain hate and the remaining 4.25% of the posts contained hate. Finally, 72.05% of the posts did not indicate any anxiety/stress, and the remaining 27.95% of the posts represented some form of anxiety/stress.

Older approaches used to do this by training a model to output a start and end index of the location of the answer in the context. However, the introduction of Transformers has made this approach obsolete.

Every day, millions of articles and papers are published. While there is a lot of knowledge hidden in those articles, it is virtually impossible to read all of them. Even if you only focus on a specific domain, it is still hard to find all relevant articles and read them to get valuable insights. However, there are tools that could help you avoid manual labor and extract those insights automatically. I am, of course, talking about various NLP tools and services. In this blog post, I will present a solution of how you can combine the power of NLP with knowledge graphs to extract valuable insights from relevant articles automatically.

The translated result is sent to the result queue. The Vision API can detect and extract text from images. You can actually do a lot of things with the help of the Google Translate API ranging from detecting languages to simple text translation, setting source and destination languages, and translating entire lists of text phrases. In this article, you will see how to work with the Google Translate API in the Python programming language. What is Artificial Intelligence: According to the Merriam-Webster dictionary, Artificial Intelligence is "a branch of computer science dealing with the simulation of intelligent behavior in computers" with "the capability of a machine to imitate intelligent human behavior".

This course will help you to learn how to use Google translator API. You will learn how to set up your computer to auto translate your files from one to many different languages. We will learn by translating closed captions or *.vtt files but you can translate any other text. If you have subtitles files for your videos which you want to auto-translate to many different languages then it's the course for you! You will be able to translate those files right away. We will use R software as our programming environment which will allow us to achieve our goal with minimum effort possible. This course is designed for you to quickly achieve your goal to know how to setup your computer in order to automatically translate your text. Join this course because you will get all these additional benefits: You learn to setup Google Cloud Platform API key You will learn how to encrypt and securely use your API key with your scripts You will learn strings manipulations in R You will automate your translating tasks Finally you will be able to make computer doing all job even when you are sleeping!!! Google Translate API is a paid service, however you can use your bonus credit from Google to start learning and applying your knowledge.

However these vectors are monolingual; meaning that while similar words within a language share similar vectors, translation words from different languages do not have similar vectors. In a recent paper at ICLR 2017, we showed how the SVD can be used to learn a linear transformation (a matrix), which aligns monolingual vectors from two languages in a single vector space. In this repository we provide 78 matrices, which can be used to align the majority of the fastText languages in a single space. This readme explains how the matrices should be used. We also present a simple evaluation task, where we show we are able to successfully predict the translations of words in multiple languages.

Incase you missed it, here are the prequels to this article about the Mobile Vision API. The first post was on the Face Detection API while the second was on the Barcode Detection API. According to the overview, the Text Detection API allows for detecting text in images and videos and it breaks down those texts into blocks (paragraphs/columns), lines (sets of words on the same vertical axis) and words (set of alphanumeric characters on the same vertical axis). The API recognizes text in various Latin based languages. I'll write about what's possible with this API before I go ahead to explain how to use it .