Introduction to Text Mining and Analytics The answer to “why text mining?” is “welcome to the age of too much information”. In today’s time, we can mine huge amount of data and easily retrieve far more relevant information from than is humanly possible to read. It is further used for text analytics which involves application of text mining techniques to solve business problems. The importance of text mining based solutions has risen owing to large number of web-based applications which generate large amount of data. It helps understand how to analyze and unlock valuable insights from the growing amount of textual content online.
Text Mining vs. Data Mining
In text mining, the prime goal is to deep dive into the text and discover information,
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Data Acquisition: Acquiring and cleaning the data, Format parsing and XML generation. Identifying text characteristics and document clustering.
2. Text pre-processing: Transforming the data by text clean ups.
3. Tokenization: Process of identifying token/topics within input documents, thus the operator splits the text of a document into a sequence of tokens.
4. Text representation: After transforming data into structured form, an effective model for document representation is needed for an efficient classification system. One of the basic methods for text representation is “Bag of Word” which forms a vector using the frequency of count of each text in the document.
5. Feature Selection: Process of selecting a subset of features (dimensions) used to represent the entire data. The associative feature selection is based on theory of separating relevant and irrelevant terms. These terms should then be assigned with a high score so that they are considered as relevant terms. On the contrary, terms that occur infrequently in association rules should be assigned with a low score of irrelevant terms. Then select features which are greater than a pre-defined threshold.
Text Mining
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Topic Tracker: Maintaining profile of users and based on the different topics the user browses through, system predicts other topics of interest to the user.
5. Scientific Advancements: Facilitate concept-assisted search for biomedical literature and work on hypothesizing causes of rare diseases. Challenges in Text Mining
• Major challenge faced in text mining is the complexity arising from the natural language itself. Natural language is not free from the problem of ambiguity; it contains words which have the capability of being understood in two or more possible senses or ways. Ambiguity gives a natural language its flexibility and usability; therefore it cannot be entirely eliminated from the natural language.
• Semantic analysis methods used for text mining purpose are computationally expensive and operate in the order of a few words per second. This poses a challenge as to how semantic analysis can be made efficient and scalable for very large text corpora.
• Multilingual text is another setback, text refining algorithms are needed to refine multilingual text documents and produce language-independent intermediate forms.
• Additionally, legalities associated with laws pertaining to copyright and usage of databases may pose hindrance to data mining if permission of the owner is not
In his book, Meyers recounts the stories of numerous scientists and researchers who discovered something magnificent when merely looking for something else. This is not to say that all of the scientists did something wrong in their research, but Meyers argues that the beauty of serendipity in medical discoveries is that no matter where the discoveries come from or how they come about, all it takes is someone intelligent and creative enough to understand the true application of the discovery. Meyers writes about four different categories of discoveries in his book. The first category is full of historical medical discoveries mostly focusing on diseases and antibiotics. The second and most relevant category is about the numerous advancements in cancer treatment. The third category focuses on one major organ: the heart which requires major focus in modern American healthcare. The fourth and last category discusses different psychotropic drugs that have been discovered as well as their uses in medicine. One of the most interesting discoveries that Meyers discusses is written within the second
Anchor standard 3: Analyze how and why individuals, event, and ideas develop and interact over the course of a text.
New and emerging diseases may be first described as a case report. They may serve as early indicators of novel developments, risks and diagnostic and therapeutic options. Individual case reports are useful for formulating hypothesis and are extremely helpful in the acquisition of evidence in the absence of any other information. Case reports are important particularly in studies of harm (unwanted events) which could not be readily studied in an intentional manner, or because of their rarity, cannot be studied prospectively (Elamin and Montori, 2012). However, the
Nuccitelli, R., Guerra, E. and Fernandes, C. (2010) “Parsing XML Documents in Java using Annotations”. In XML: Aplicações e Tecnologias Associadas, 8, Vila do Conde.
Information Retrieval (IR) is to represent, retrieve from storage and organise the information. The information should be easily access. User will be more interested with easy access information. Information retrieval process is the skills of searching for documents, for information within documents and for metadata about documents, as well as that of searching relational databases and the World Wide Web. According to (Shing Ping Tucker, 2008), E-commerce is rapidly a growing segment in the internet.
The evolution of studying and documenting genetic diseases in the scientific community has been one of exponential growth, this is important because it helps communities
By searching words in the COCA Corpus, and clicking on the list option, a list of sentences with the searched word appears. This is very important for learners of English because the Corpus can be helpful in order to figure out the meaning and use of a word, the grammatical function, as well as words combinations. For instance, if I want to know in what situations the word “useful” is used, I need to click on the chart option, and after the search, I will be presented with a chart that tells me in what contexts the word is used. In this case it is mainly used in academic and magazine contexts and now with this search I know that it is appropriate to use the word “useful” in an academic paper or work. This can be interesting for teachers because they can use Corpora to show students the words that are appropriate in an academic context.
For advertisers, Data Mining can turn into a valuable tool with the emerging new media of internet, blogs, podcasts and search ads (as opposed to the traditional media, such as television, radio, or newspapers). The incre...
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
Our every day life has changed forever, thanks for the ubiquitous smart phones and technology dependent information age. We leave a trail of data while travelling, shopping, driveing, bloggin, and even voting. All of these activities leave a digital signature unique to us, which if analyzed can predict our next move. Similarly, a large set of data is being created each day by businesses, researchers and the World Wide Web. According to an estimate by the government, there are about 1.2 zettabytes (250 billion DVDs) of electronic data generated each year by everything from underground physics experiments and telescopes to retail transactions and Twitter posts (Mervis 22). This data growth has created a new challenge and opportunity. The challenge is that, we don’t have sufficiently trained people to analyze Big Data. The opportunity is that if you have the right resources who can transform Big Data to a meaningful use, you guarantee the success of your business.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
My future plans are to become a biomedical scientist. Biomedical scientists serve in all levels of health science research from basic science working with cell cultures to human clinical trials experimenting the most cutting-edge breakthroughs to maximize human quality of life. I am interesting in researching rare diseases. There are many rare diseases that affect small proportions of the population and unfortunately go unnoticed for reason. Unfortunately, usually it is because therapeutic agents for these diseases would have a much smaller market than therapeutics for common diseases. There is an immense need for research attention to ultimately improve clinical outcomes in patients of rare diseases because many are a lifelong chronic prognosis and are genetic and non-modifiable by lifestyle factors and are lacking of often absent of current treatment options. The government helps by socializing the solution by having money set aside by public National Institute of Health (NIH) to research ‘orphan diseases’. An example of one such program from the NIH is Orphan Products Natural History Grants Program.
Word processing typically implies the presence of text manipulation functions that extend beyond a basic ability to enter and change text. For the purpose of this module when discussing and explaining word processing features the software Microsoft Office Word (*Word) will be used as the example.