Sentiment analysis and text mining using Azure Cognitive Services
This post provides 6 sample codes showing how Azure Microsoft Cognitive Services can used to build AI solutions to tackle opportunities like sentiment analysis and text mining.
What is Sentiment Analysis?
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. (Source: Wikipedia)
What is Text Mining?
According to Hotho et al. (2005) we can differ three different perspectives of text mining, namely text mining as information extraction, text mining as text data mining, and text mining as KDD (Knowledge Discovery in Databases) process.[1] Text mining is “the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources.”[2] Written resources can be websites, books, emails, reviews, articles. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. ‘High quality’ in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). (Source: Wikipedia)
What is Azure Cognitive Services?
Cognitive Services bring AI within reach of every developer — without requiring machine-learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand and accelerate decision-making into your apps. (Source: Microsoft)
Azure Cognitive Services Sample Codes
Clicking here, you will find 6 sample codes using Azure Cognitive Services for:
- Sentiment Analysis using Azure Cognitive Services
- Detect Language using Azure Cognitive Services
- Extract Key Phrases using Azure Cognitive Services
- Recognise Entities using Azure Cognitive Services
- Recognise Linked Entities using Azure Cognitive Services
- Recognise PII Entities using Azure Cognitive Services
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