WIth the numerous technological advancement and its colossal deployment produces the gigantic amount of unstructured text data digitally. This sort of data contains valuable information and knowledge.
Therefore, in order to extract such an amount of knowledge from unstructured text data , a data expert needs to perform mining techniques over textual data. Text mining is the process of extracting hidden, priorly unidentified, and significantly useful information from unstructured textual data.
Also referred to as Text Data Mining, Text Analysis, or Knowledge Discovery in Text, the approach of text mining is applied on unstructured or semi-structured data in the form of text file, pdf file, email, online chat, SMS, product review, xml & html files, and many more.
What are Text Mining Techniques?
The process of text mining involves various activities that assist in deriving information from unstructured text data. Text mining techniques can be explained as the processes that conduct mining of text and discover insights from the data. These techniques deploy various text mining tools and applications for their execution.
Even before applying several text mining techniques, one should perform text preprocessing. It is the process of cleaning and interpreting data into its implementing format.Being a core aspect of NLP, text preprocessing comprises the use of many techniques such as language identification, tokenization, part-of-speech tagging, and many more.
After the completion of this process, some text mining techniques can be applied, as following, in order to derive insights from data.
Text Mining Techniques
Information Extraction (IE)
It is the technique used to extract valuable information from a massive amount of data. IE is the starting step for systems to decipher unstructured text by discovering key phrases and relationships within text, and involves the tasks as tokenization, identification of named entities, sentence segmentation, and part-of-speech assignments.
For this IE systems are practised to bring out specific information, attributes and entities from the document and recognise their relationship. After this, the extracted corpora are accumulated into associated databases for additional processing. In order to inspect and evaluate the pertinent information/outcomes from the extracted data, precision and recall process are used.
Deep and extensive information is necessary about the connected field to process information extraction methods for achieving maximum outcomes.
Information Retrieval (IR)
IR is the process of extracting out pertinent information and connected patterns from the given set of words or phrases. In information retrieval, different algorithms are deployed for tracking the user’s behaviour and discover relevant data and information accordingly.
For example, Google Search Engine uses information retrieval systems consistently for deriving relevant documents according to phrases on the web. For this purpose, search engines implement query based algorithms to maintain the trends and achieve more associated results. After that, search engines provide more relevant and accurate information to users according to their search needs.
Natural Language Processing
NLP deals with the automatic processing and analysis of unstructured textual information and allows computers to read via analyzing sentence structure and grammar. It performs various types of analysis such as NER, summarization, sentiment analysis, as below
Summarization: To give synopsis of huge textual data for making a concise, and intelligible summary of substantial points of a document.
Part-of-Speech (PoS) tagging: To allocate a tag for each word/token in a document on the basis of its part of speech as specifying nouns, verbs, adjectives, etc. PoS tagging permits semantic analysis over unstructured text.
Text categorization: To analyze text documents and classify them on the basis of predefined topics or categories and benefits when categorizing synonyms and abbreviations.It is also known as text classification.
Sentiment analysis: To determine positive or negative sentiment from inside/outside data sources, and allow users to trace changes in customer behaviour over a specific time period. In order to obtain relevant information regarding perceptions of brands, products, and services, sentiment analysis is used and hence propel organizations to connect with customers to improve processes, user experience & satisfaction.
Clustering method is an unsupervised process that classifies text documents into groups through applying various clustering algorithms. What happens in clustering is similar terms or patterns are organized and extracted from several documents where clustering is conducted in top-down and bottom-up manner.
As a result, distinct partitions, called clusters, are generated and each cluster has a number of documents. The content of each document in a single cluster is very similar and content in different clusters are dissimilar such that the quality of clustering is accounted for better.
A fundamental clustering algorithm keeps track of topics for each document and measures the weightage of how better the documents fit into each cluster.
The quality of a clustering result relies on similarity measures of text content used by the clustering method and its implementation such that a good clustering method generates a great quality of clusters with high intra-cluster similarity and low inter-cluster similarity.
It is different from categorization as in clustering, text contents are clustered without previous knowledge of classes. The main advantage of clustering is that text content can be relevant to multiple classes.
Different clustering techniques are hierarchical, distribution, density centroid, and k-means clustering, used for analyzing unstructured text documents.
Under the categorization method, one or more categories of independent (free format) text documents are assigned. Depending on the input-output examples to discriminate new documents , categorization is considered as supervised learning method. Based on the texts content, predefined classes are assigned to each text documents,
The process of text categorization involves methods such as pre-processing, indexing, dimensionality reduction, and classification with the objective to train classifiers on the basis of recognized examples and then unrecognized examples would be categorized automatically. Also, text categorization faces the difficulty of high dimensionality of feature space.
Some useful analytical classification models, used to categorize text, are naive bayesian classifier, nearest neighbor classifier, decision trees, and support vector machines. Applications included in categorization are document organization, spam filtering, SMS categorization, and hierarchical categorization of web pages.
Visualization methods can improve and clarify the analysis of relevant information. In order to outline individual documents or clusters of documents, text flags are practised to show the category of documents and colors are used to show document density.
In this method, large textual sources in a visual hierarchy so that a user might interact with the documents via diving and scaling. For example, the government uses information visualization to detect the terrorist networks and to identify crime-information.
The process of visualization technique has three steps;
Data preparation: This step involves determining and obtaining original data of visualization and creating original data space.
Data Analysis and Extraction: The process of evaluating and extracting visualization data, required from original data, and to form visualization data space is termed as Data Analysis and extraction.
Visualization Mapping: This step takes some mapping algorithms for mapping visualization data space to visualization target. (from)
With the fundamental aim to decrease the length, details and complexity of a document while keeping significant points and actual meaning, text summarization helps in dealing whether a lengthy document accomplishes the user’s requirements or not and also in resolving whether it is worthwhile reading for further information or not, and hence text summary could be replaced by groups of documents.
Whenever a user reads a first paragraph, text summarization software handles and summarizes a large text document in less time than to users. It can be classified into two parts;
Abstractive Summarization: It creates a clear perception of key concepts in the text and depicts those concepts in the natural language. It employs linguistics methods for understanding, transforming and explaining text into precise form.
Extractive Summarization: These are conducted via deriving major text segments, relying on statistical analysis of text features such as words/phrases frequency, position or suggested words to detect the sentences to be extracted.
In particular, text summarization is three steps process;
Pre-processing: This step makes structured representation of actual text. Tokenization, stop word removal, and stemming are some methods, applied for pre-processing.
Processing: Algorithms are applied in order to translate and interpret summary structure out of text structure.
Development state: This step includes retrieving the final summary from summary structure.
With the increasing amount of text data, effective techniques need to be employed to examine the data and to extract relevant information from it. We have understood that various text mining techniques are used to decipher the interesting information efficiently from multiple sources of textual data and continually used to improve text mining process.
According to the business problems and requirements, appropriate selection and use of techniques and tools should be done in order to make the text mining process easy and efficient.