{"id":5000,"date":"2014-04-14T08:48:01","date_gmt":"2014-04-14T03:18:01","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=5000"},"modified":"2024-11-11T17:00:51","modified_gmt":"2024-11-11T11:30:51","slug":"spotlight-text-analytics","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/spotlight-text-analytics\/","title":{"rendered":"Spotlight: Text Analytics"},"content":{"rendered":"

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What do you do when you have a huge volume of textual data? <\/span><\/p>\n

If the data is in structured form as in voter records you make a query that returns information as a number. Using more and different queries, you get detailed <\/span>\u00a0<\/span>information; such as number of female voters in a particular constituency above the age of 50 and so on. However, when the data does not conform to a common schema as in documents, emails, tweets and blogs i.e. unstructured data, a \u2018keyword search\u2019 is made that returns information analysed for contextual significance.<\/span><\/p>\n

Text mining<\/span><\/b> = analysis of both structured and unstructured data to extract meaningful associations, trends, and patterns in large volumes of different text sources.<\/span><\/p>\n

\n\n\n\n\n\n
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Type of Data<\/span><\/b><\/p>\n<\/td>\n

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Method of retrieval<\/span><\/b><\/p>\n<\/td>\n

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Analysis<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Structured<\/span><\/p>\n<\/td>\n

\n

Query: Returns data<\/span><\/p>\n<\/td>\n

\n

Data mining: Insight from structured data<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Unstructured<\/span><\/p>\n<\/td>\n

\n

Search: Returns documents<\/span><\/p>\n<\/td>\n

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Text analytics: Insight from text<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

While text mining uses algorithms that are more broad-based, \u00a0t<\/span>ext analytics is more goal driven.<\/span><\/p>\n

The massive volume of unstructured digital data in social media ecosystems like Twitter, Facebook and Flickr, offers great scope for investigating social, cultural, economic or political behaviour. \u00a0For instance, a\u00a0business may apply text analytics to assess the feedback of a new product launch, while a news channels will apply different techniques to gain insights during elections. Such as, analysing the Twitter response to the mandate of a political candidate \/party using the hashtags\u00a0 #ModiManifesto, #CongressManifesto, and deriving insights into the focus of a manifesto using certain keywords.
\n<\/span><\/p>\n

\"manifesto\"<\/a><\/p>\n

So how would you define text analytics?<\/span><\/b><\/p>\n

Text analytics<\/strong>\u00a0is the process of analysing unstructured text, extracting appropriate information and transforming it into a structured form that can then be leveraged in various ways. The analysis and extraction processes apply techniques that originate in computational linguistics, statistics and other computer science disciplines. For huge volume of data, automated tools are applied, both commercial\/proprietary and free to\u00a0<\/span><\/em>download and use.\u00a0<\/span><\/em><\/p>\n

Processes \u00a0<\/strong><\/p>\n

\"TextAn_2\"<\/a><\/p>\n

Techniques<\/strong><\/p>\n

\"TextAn_1\"<\/a><\/p>\n