By Timo Selvaraj
Natural language processing is gaining momentum with the increasing volume of text information and the cheaper cost of infrastructure to process big data for insights on employees, customers and users. Machine training and learning technologies like Classification, Recommendations and Predictive Analytics that worked well with structured data in the past, are being tested with unstructured text like customer feedback, reviews, ratings, likes and comments in increasing number of organizations with the goal to automate the process of finding themes and extracting concepts for actionable insights.
Here are some of the popular natural language processing techniques in use within organizations.
Reading a customer review or a comment for concept extraction for a few reviews manually can be fun. Imagine doing that on a daily basis for tens of thousands of reviews across the web. Content crawlers can seek out and summarize concepts from across web and social sites on a daily basis. Machine training and learning can be employed to identify popular themes and classify for insights. Once the themes are identified, filtered and sorted for reporting, staff can visualize them for presenting them to senior leadership or the appropriate teams for corrective measures or commend staff for a job well done with positive reviews.
Another popular technique used with natural language processing is identifying the named entities and their relationships where specified. This could be identifying a place or organization or person. Identifying the named entities in different pieces of text could be key in establishing relationships between them or sequencing a timeline for analysis. This also helps visualize the correlations between the name entities and their relationships. A great example of this would be customers visiting a certain store location at a certain time of day and the experiences are inconsistent with the expectation set by the organization. When the named entities are identified in the text, then it helps identify the patterns of behavior experienced and corrective measures can be implemented saving revenue loss or brand image degradation.
A key NLP technique used by many publishers is automatic summarization of a page or document for quick reading. This helps provide a snippet of text to tease the appetite of readers and then leads into a larger document or the full page. This can also be used to create automated text ads or offer relevant advertising on websites without having to manually classify the web content.
IBM’s Watson made this NLP method very popular with the famous Jeopardy show. Questions are setup and the best responses to each question are assimilated and ranked by the end users. This process can also be setup as guided answers where the questions and answer snippets are added into the system and allow the machine to learn from the users feedback ratings. This algorithm is scored by users and the system learns continuously with a fixed corpus of text.
Positive or Negative themes in comments can be quickly analyzed through this technique. This is a very useful technique for brand monitoring, mystery shopping or customer/employee feedback or review. Apart from assigning a positive or negative labeling, machine learning can also assign scores with the level of positivity or negativity in the message resulting in the ability to analyze and visualize the text further.
SearchBlox provides the enterprise platform for indexing, search and text analytics of multiple data sources for actionable insights. NLP techniques that are discussed in this post can be applied to your textual content to achieve the required benefits for your organization. Contact us to learn more about how you can enable text analytics and machine learning for your organization.