Zia Features and ML algorithms

QuickML mainly focuses on powering ML Pipelines with Machine Learning Operations in an effortless manner to provide smooth pipeline execution environment. Hence, it has been integrated with a wide range of ML technologies and features to provide the best analytical results out of data. The ML capabilities included in QuickML are all in-house Zia features and general algorithms.

Zia features

QuickML is integrated with market-hot Zia text analytics features that are in-house developed. The Key features in these categories are:

• Zia Sentiment Analysis
• Zia Keyword extraction
• Zia Language detection
• Zia Emotion detection
• Zia Intent Extraction
• Zia Activity Extraction
• Zia Commitment Classification

Zia Sentiment Analysis:

Zia Sentiment Analysis is a part of text analytics that processes textual content to recognize the tone of the message, and the sentiments it conveys. It analyzes each sentence to determine if its tone is positive, negative, or neutral. It then determines the tone of the overall text as one of these three sentiments, based on the sentiments recognized in each sentence.

Zia Keyword extraction:

Keyword Extraction is a text analysis technique that involves extracting important and relevant terms from a piece of text, which provides an abstraction of the whole text. It also works on the principles of text mining, information retrieval, and natural language processing. Keyword extraction is similar to the areas of analyzing human language and developing precision with more training using rich data sets. It uses simple statistical approaches like word frequencies and collocations, as well as advanced machine learning approaches.

Zia Language detection:

In natural language processing, language identification or language detection is the technique of determining which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization. Language detection is a great use case for machine-learning more specifically for text classification. Given some text from an email, news article, output of speech-to-text capabilities, or anywhere else, a language detection model will tell you what language it is in.

Zia Emotion detection:

Emotion detection is a subset of sentiment analysis, as it predicts the unique emotion rather than just stating positive, negative, or neutral. This is a technique for figuring out people’s attitudes, emotions, and sentiments about a certain objective or thing. Zia’s emotion predictor makes it easier to identify and analyse the emotions hidden in textual data. Happy, Enthusiasm, Discontentment, Frustration, Trust, Confusion, Gratitude, and Neutral are the 8 emotions that Zia’s algorithm can anticipate with good degree of accuracy. Give a sentence, Zia’s emotion detection model will predict and tell what kind of emotion is involved in that sentence.

Zia Intent Extraction:

Intent extraction model is a deep learning model using Distilbert Transformers architecture, trained on a large set of English sentences that assist to determine the kind of action being expressed in a phrase as well as all of its constituent elements. Intent detection is the process of analysing text data to find the author’s intention. Zia’s intent extraction model can identify terms in a sentence that are relevant to complaints, requests, purchases, and queries.

Zia Activity Extraction:

Zia’s activity extraction is a multi-class sequence classifier which will recognise activities such as an event, call and task from a given sentence by utilising the Distilbert classifier.

Zia Commitment Classification:

Zia’s commitment classification will predict and identify the commitment-related clauses in a phrase, such as the due date, the promise of something, etc. Give any sentence, Zia’s commitment classification model will detect and will return the commitment statement.