(Part of Pure Analyzer Product Suite)
The Text Analyzer module analyzes social media, news, and research content such as tweets, posts, likes, updates, discussions, product reviews, weblogs, emails, customer surveys, custom complain, website traffic or customer database. The goal is to review subjective opinions, feelings and attitudes relating to a given brand, topic, or keyword and extract meaningful data measurements.
Let us help your business to differentiate your product/service using the power of text analytics. Call us to understand how different companies have used machine learning to grow their business.
Text Analyzer Component Modules include the following:
Categorize, cluster, prioritize, and harmonize data to analyze big data. It can save hundreds of hours compared with the efforts of a human analyst. Here are some examples of benefits we can achieve using categorization:
- Sentiment categories such as positive, negative and neutral.
- Data can be classified as Relevant vs. Irrelevant. This is very useful in analyzing customer comments and emails.
- Customer lifestyle interests such as sports, music, and travel.
- Business area categories such as corporate governance, pricing, market insight, financial issues, legal issues etc.
- All these features are also available as API for easy integration to your custom solution as well.
Excel PlugIn is available to get the power of machine learning to your excel data such as customer comments and feedback. Get the sentiment of text in excel just by typing “=findsentiment(text cell reference)”.
Entity Extraction and Topics API
Entity extraction and topics api helps in executives get a thousand foot view of the data in real-time.
Who, when, where, and what knowledge extraction from text. We can provide the ability to group individuals based on their network of family, friends, and other interest groups. This can provide insight about what folks are involved in the conversation and who is related to whom at what level, which can help us, whether in maximizing the impact of corporate marketing efforts or in more accurately assessing insurance risks. Here are some of the specific characteristics we can help isolate:
- Topic Extraction
- Entity extraction (Entity defined as person, location, money, organization, date, email address, URL, SSN, etc. )
- Finding relationships among entities
Document Summarization API
Executive summary of a document for selecting the right document in enterprise search makes all the difference. Document summarization api has saved thousand of hours of our clients by just doing that.
Pure Customer Service Plugin for Outlook
When you have a dissatisfied customer, time is of essence. Our Pure Customer Service plug-in (PCSP) for Outlook allows customer service representatives to identify emails from dissatisfied customers without the need to read each email.
Pure Customer Service plugin is easy to install plugin in Microsoft Outlook. Once installed, its advanced artificial intelligence algorithms categorize all incoming emails on multiple dimensions such as urgency, customer inquiry, promotional email, and Positive/Negative sentiment. All these categories are visible to the customer in a column next to email title in Microsoft Outlook. With our plugin, you are in charge of prioritizing the work.
Try out our FindGender API to improve filter results of data searches. This is a prefect tool, for example, for a customer who is trying to put together a health insurance provider network – to improve the quality of the provider directory.
Industry Specific Models
Switch analysis for Telecom industry tells which customers are indicating that they are thinking about switching cell phone carriers and helps isolate the reason for the switch. Our clients have used this model to gain customer insight, general leads, retain customers etc. Below is a graphic which illustrates the value proposition for this type of analysis, using the example looking at why certain customers switched from one mobile phone carrier to another:
Text analyzer clusters documents in multiple groups for easy pattern recognition, and statistical data analysis. The system’s ability to recognize relationships between persons and products is a powerful tool making it easier to target specific customer segments.