Text and Sentiment Analytics
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Text and Sentiment Analytics
Text Analysis is the contextual mining of text which identifies and extracts subjective information in the source material. It helps a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. Analysis of social media streams is usually restricted to basic text analysis and count-based metrics. With the recent advances in deep learning, the ability of algorithms to analyze text has improved considerably. The creative use of advanced artificial intelligence techniques is an effective tool for doing in-depth research. We believe it is important to classify incoming customer conversation about a brand based on the following lines:
- Key aspects of a brand’s product and service that customers care about.
- Users underlying intentions and reactions concerning those aspects.
These basic concepts when used in combination become an important tool for analyzing millions of brand conversations with human-level accuracy. Text Analytics can be used for a number of applications including categorization, sentiment analysis, and entity extraction.
Sentiment Analysis is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative, or neutral.
Intent analysis steps up the game by analyzing the user’s intention behind a message and identifying whether it relates to an opinion, news, marketing, complaint, suggestion, appreciation, or query.
Contextual Semantic Search (CSS)
To derive actionable insights, it is important to understand what aspect of the brand a user is discussing about. An intelligent smart search algorithm called Contextual Semantic Search (CSS) is used. The way CSS works is that it takes thousands of messages with a concept as an input and filters all the messages that closely match with the given concept.
Categorization is a core function of text mining software. Categorization is capable of showing you everything from the broader picture down to the minutiae that drive informed business decisions. Categories help you sort large volumes of text without actually reading them. Take 10,000 consumer tweets and classify them under politics, gaming, religion, food, or whatever else the consumers are discussing.
Most of the organizational knowledge is stored in the form of documents. Entity extraction is the foundation for knowledge-intensive corporations. Entity recognition (also known as entity identification, entity chunking, or entity extraction) is a subtask of information extraction that seeks to locate and classify entity mentioned in unstructured text into pre-defined categories such as the person names, organizations, locations, medical codes, time expressions, quantities, monetary values, and percentages.
These are the most common uses, depending on your organization they can be used for much more. Get in touch with us to understand how mCycloid can help you gain insight into your external and internal text data.