Sentiment Analysis

Organizations have often found it tricky to accurately figure out what emotions their customers go through when they come in contact with their brand, use their products or experience their services. Now, with multiple online customer interaction platforms, understanding customers’ sentiments has become difficult as well. Sentiment Analysis allows you to determine whether the customer’s mood is positive, negative or neutral by analyzing their comments or responses on social media platforms, user feedback channels, customer support, chatbots and user reviews platforms. Accurate sentiment analysis generates extremely valuable insights for both businesses and advertisers. These insights or feedback help them formulate better business strategies and take informed business decisions. AI-based sentiment analyzing models often find it difficult to ascertain the nuances of human language. Such analyzers first need to be trained on high-quality annotated data to accurately analyze sentiments. With a team of highly skilled data annotators and access to a large crowd of data contributors, we are well-positioned to provide quality training data for sentiment analysis services to its clients.

Intent Variation

Human languages are very complicated. Depending on the speaker, its mood, situation and culture, words have different meanings in different contexts. To make conversational AI models like chatbots understand the intricacies of human languages, they need to be trained. They must be trained to understand the intent behind different statements said by the user. An intent is a group of utterances or words with similar meaning. Natural Language Processing, or NLP, is the process which enables machine learning models to make sense of different human languages, recognize the intent in similar statements despite the order of words or the structure of the sentences, and produce a suitable response. To train machines to detect intents accurately, the training datasets must be comprehensive and diverse. We work on such custom datasets that cover all the possible ways through which users from different backgrounds and age might express their intent. These training datasets are ideal for training machine learning-based models like search engines, chatbots and voice assistants.