TRAC supports over 100 languages, including both commonly spoken languages like English, French, and Spanish, as well as less common ones like Icelandic and Tagalog. This means that TRAC can help you analyse online conversations in a wide range of languages, providing you with a comprehensive view of the online discussions happening around your topics of interest. These multilingual capabilities allow you to analyse social conversations in a wide range of languages, without needing to manually translate them. This saves time and allows you to analyse conversations in real-time, regardless of the language they are in.

You can find more information on the locations and languages we cover below:

TRAC's language analysis capabilities are extensive and can you help you to extract a wide range of insights from the discussions taking place online. With the use of NLP, our language analysis goes beyond simple detection and can analyse the common keywords, topics, sentiment, entities and emotion found in an article or media clip. This can help you gain cross-cultural understanding and a better view of the conversations happening in different parts of the world. So, when we talk about language analysis on Pulsar, we are talking about the ability to detect the language, and the ability to analyse the sentiment, emotion, topics and entities found in a content, regardless of the language.

TRAC can identify the sentiment behind social conversations and help you understand how people feel about your brand or a specific topic. This can be particularly helpful for monitoring brand reputation and identifying issues before they escalate. If you want to learn more about Sentiment Analysis on TRAC, then use the link below.

Emotion Analysis is a standard part of natural language processing and involves analyzing the underlying emotions expressed in text. These emotions can range from joy, excitement and surprise to fear, hate and disgust. If you want to learn more about Emotion Analysis on TRAC, then use the link below.

Using a top-down taxonomy approach, TRAC can identify the key themes found in conversations in a range of languages, even if they aren’t explicitly mentioned, helping you to understand what people are talking about and identify emerging trends. If you want to learn more about Topics Analysis on TRAC then use the link below.

Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) and as the name suggests, is the process of identifying and classifying units (named entities), in a given text. And these entities can be words or phrases that represent names of people, names of organisations, places, products, etc. TRAC can identify specific entities mentioned in online conversations across a range of languages. If you want to learn more about Entities, then you can click on the button below.