8/6/2019 Automating Your Text Analytics and Text Mining Operations

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2040 14th Suite 200 Boulder, CO 80302 720-259-3060 http://www.collectiveintellect.com

Our Client’s GoalsCollective Intellect (CI) worked with a

leading digital media company to assist intransforming a manually intensive textmining operation into an automated, real-time, sophisticated review of consumer text.The client’s previous system relied on a teamof individuals reviewing each text file andassigning a tag to each post that was relatedto a specific annotation. They knew thismethod was not scalable but were unsurehow to derive the precise insights necessaryfrom an automated process.

ResultsCI was able to configure our sophisticatedsemantic engine to not only analyze the largevolume of content but also surpass theaccuracy of the client’s manual in-houseteam. Using the same tagging methodology CI

mapped the tags to a dimension. Dimensions are used to extract specificaspects of a text; in this case, our text analytics technology was configured toextract content around each of the tags. The automated approach was not onlymore accurate than the previous manual process the client employed but wascompleted more quickly and could be scaled to accommodate much greatervolumes of data. Collective Intellect utilizes Latent Semantic Analysis (LSA)

Project Highlights

Goal – Automate a

manually intensive textmining operation todeliver better accuracy forconsumer insights as an in-house team but faster andwith a built-in scalability.

Results – Our semanticand text analyticstechnology was ablesurpass the accuracy of thein-house text mining teamand seamlessly integratethe resulting data into theclients data managementsystem.

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2040 14th Suite 200 Boulder, CO 80302 720-259-3060 http://www.collectiveintellect.com

and Natural Language Processing (NLP) to deliver highly accurate analysisand associated observations from unstructured data. Whether it’s socialmedia, email, public or private community data, chat transcripts or surveys, ifdata is text then CI possesses the capability to run analysis.

How CI Approached the Project

Working with our client, CI developed a text analytics strategy that mappeddimensions to each of their tags, which were then used to annotate each post .

Data was fed directly into our semantic engine, tagged and then exported backto the client. Once the analysis was complete, the resulting information wouldbe fed into our client’s existing data system.

Suggested Next StepsCI recommended to our client that theyconsider correlating their private data withsocial media data. Analyzing andcorrelating the two data sources wouldallow the client to determine if certainsignals detected within their private data

were also occurring within the social mediaecosystem or were an event unique to eachsource. Utilizing the same tags todimensions methodology, on-topic socialconversations could be collected and

analyzed. The resulting information could be integrated into an existing datamanagement system or used to inform other business critical objectives andmeasures.

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