Introduction to Text and Web Mining

I. Text Mining is part of our lives

Google trends

Google correlate

Social Metrics Insight

Related words on “bigdata”

Sentiment analysis on “bigdata”

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In March 2011, D’Aloisio created Trimit, an app that summerizes s, blog posts and more into 1,000, 500, or 140-character summaries and be able to share it via SMS, , Facebook, Twitter in.txt form in just a few clicks or shakes of your iPhone. In July of the same year, Apple named Trimit as a noteworthy app on the. App Store

II. What is text mining?

Text Mining Text mining Application of data mining to non- structured or less structured text files. It entails the generation of meaningful numerical indices from the unstructured text and then processing these indices using various data mining algorithms

Text Mining Text mining helps organizations: –Find the “hidden” content of documents, including additional useful relationships –Relate documents across previous unnoticed divisions –Group documents by common themes

Text Mining Applications of text mining –Automatic detection of spam or phishing through analysis of the document content –Automatic processing of messages or s to route a message to the most appropriate party to process that message –Analysis of warranty claims, help desk calls/reports, and so on to identify the most common problems and relevant responses

Text Mining Applications of text mining –Analysis of related scientific publications in journals to create an automated summary view of a particular discipline –Creation of a “relationship view” of a document collection –Qualitative analysis of documents to detect deception

Text Mining How to mine text 1.Eliminate commonly used words (stop-words) 2.Replace words with their stems or roots (stemming algorithms) 3.Consider synonyms and phrases 4.Calculate the weights of the remaining terms

Web Mining Web mining The discovery and analysis of interesting and useful information from the Web, about the Web, and usually through Web- based tools

Data Mining Project Processes

Web Mining Web content mining The extraction of useful information from Web pages Web structure mining The development of useful information from the links included in the Web documents Web usage mining The extraction of useful information from the data being generated through webpage visits, transaction, etc.

Web Mining Uses for Web mining: –Determine the lifetime value of clients –Design cross-marketing strategies across products –Evaluate promotional campaigns –Target electronic ads and coupons at user groups –Predict user behavior –Present dynamic information to users

Sentiment analysis (Opinion Mining) sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation on affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication.

A basic task in sentiment analysis is classifying the polarity (+, -)of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy."

Applications Detecting the polarity of product reviews and movie reviews respectively. Classifying a movie review as either positive or negative to predicting star ratings on either a 3 or a 4 star scale Analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

Social network analysis Social network analysis (SNA) is the use of network theory to analyse social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes, representing individual actors within the network, and ties which represent relationships between the individuals, such as friendship, kinship, organizations and sexual relationships. These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines. (NodeXL) network theorysocial networkssocial relationshipsfriendshipkinshipsexual relationshipsnetwork diagram

Human SNS Graph

III. Text Mining Cases Cases on text mining

IV. Text Mining Techniques R Python Open API