Noisy Text Analytics

Analytics for Noisy Unstructured Text Data I

How Much Noise in Text is too Much: A Study in Automatic Document Classification

Noise is a stark reality in real life data. Especially in the domain of text analytics it has a significant impact as data cleaning forms a very large part (upto 80% time) of the data processing cycle. Noisy unstructured text is common in informal settings such as on-line chat, SMS, email, newsgroups and blogs, automatically transcribed text from speech data, and automatically recognized text f...

Text Analytics of Customers on Twitter: Brand Sentiments in Customer Support

Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of ...

Company Mention Detection for Large Scale Text Mining

Text mining on a large scale that addresses actionable prediction needs to content with noisy information in documents, and with interdependencies between the kinds of NLP techniques applied and the data representation of instances. This paper presents an initial investigation of the impact of improved company mention detection for financial analytics. Coverage of company mention detection impr...

Analytics for Noisy Unstructured Text A Data II

The importance of text mining applications is growing proportionally with the exponential growth of electronic text. Along with the growth of internet many other sources of electronic text have become really popular. With increasing penetration of internet, many forms of communication and interaction such as email, chat, newsgroups, blogs, discussion groups, scraps etc. have become increasingly...