Raw Unstructured Data Research Articles

Feasibility of Establishing an Artificial Intelligence Based Head and Neck Cancer Registry: Experience from a Tertiary Care Hospital.

The online version contains supplementary material available at 10.1007/s12070-022-03173-3.

DATA OWNERSHIP: A MISTAKEN PERSPECTIVE TO PROTECTION OF DATA PRIVACY

With technology growing exponentially in the digital age, a person’s personal information has acquired the status of the most valuable possession for both individuals and businesses/ government. In such times when one understands the power of this resource and the kind of economic profits it brings along, the major debate is – whose data is it? Does it belong to the individual whose information is out there for grabs or does the business organizations hold the right to ownership as they put in a lot of skill and effort to create datasets in order to make sense out of the raw unstructured data collected thus? This paper tries to ascertain the ownership rights and further explains why attaching ownership rights to personal data may be disastrous in terms of protecting the individuals’ data privacy from any kind of violations

Towards unstructured mortality prediction with free-text clinical notes.

Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality prediction. This work attempts to assess the gain in performance when multiple notes that have been minimally preprocessed are used as an input for prediction. A hierarchical architecture consisting of both convolutional and recurrent layers is used to concurrently model the different notes compiled in an individual hospital stay. This approach is evaluated on predicting in-hospital mortality on the MIMIC-III dataset. On comparison to approaches utilizing structured data, it achieved higher metrics despite requiring less cleaning and preprocessing. This demonstrates the potential of unstructured data in enhancing mortality prediction and signifies the need to incorporate more raw unstructured data into current clinical prediction methods.

Dominant Lexicon Based Bi-LSTM for Emotion Prediction on a Text

User-generated content and opinionative data has become a massive source of information on World Wide Web in the past few decades. Through social media people can share more conveniently their opinions, views, feelings and attitude about a product, person or event at anytime and anywhere as daily basis. This ever-growing subjective data makes enormous amount of unstructured data in web. Analyzing emotion in this raw unstructured data gives a very fruitful information for any kind of decision making process taken by both government and industries. Sentiment or emotion analysis is a field of Natural Language Processing (NLP), is used to identify the emotion depicted (by) in the form of text. Computation of emotion and emotion intensity depicted by a text is a very difficult task. Feature extraction from the text for vector representation is a difficult step of emotion analysis because it defines the emotion accuracy of the prediction. In this paper, a selective lexicon based BI-LSTM technique has been proposed. This technique uses only the most affected lexicon and its features for final vector representation. This method is a combination of features collected from the convolutional Neural Network (CNN), Long Short Term Memory (Conv - LSTM) and Bidirectional Long Short Term Memory (BI-LSTM). As a result the proposed model Selective Lexicon Based BI-LSTM (SL + BI-LSTM) outperforms all the models with high accuracy.

Knowledge Generation Using Sentiment Classification Involving Machine Learning on E-Commerce

Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.

Identification of the CIRP expertise network based on public data

Identification of expertise of people, academic societies, companies, and institutions is an important professional and social activity for nurturing professional networks, forming project consortia, etc. Today, a lot of information about the skills and expertise is openly available from websites and public personal profiles on professional and scientific social networks. With computerised methods, such as web crawling, text mining, and keyword classification, it is possible to identify expertise networks from the raw unstructured data. The paper presents and analyses the extended expertise network of the CIRP scientific community and proposes a web-based tool for the structuring and management of the community’s expertise.