Hire realcode4you expert to get best data analysis assignment help. Here we have learn about some important and useful Text Mining and Analysis topics which is necessary to start Natural Language Processing.
During this lesson the following topics are covered:
Challenges with text analysis
Key tasks in text analysis
Definition of terms used in text analysis
Term frequency, inverse document frequency
Representation and features of documents and corpus
Use of regular expressions in parsing text
Metrics used to measure the quality of search results
Relevance with tf-idf, precision and recall
Text mining, also known as text analysis, is the process of transforming unstructured text data into meaningful and actionable information.
Data helps companies get smart insights on people’s opinions about a product or service. Think about all the potential ideas that you could get from analyzing emails, product reviews, social media posts, customer feedback, support tickets, etc. On the other side, there’s the dilemma of how to process all this data. And that’s where text mining plays a major role.
Text Mining process
Text mining combines notions of statistics, linguistics, and machine learning to create models that learn from training data and can predict results on new information based on their previous experience.
Basic Methods Which Used For Text Analytics
Word frequency can be used to identify the most recurrent terms or concepts in a set of data.
Finding out the most mentioned words in unstructured text can be particularly useful when analyzing customer reviews, social media conversations or customer feedback.
For example, if the words “Expensive”, “Overpriced”, and “Overrated” frequently appear on your customer reviews, it may indicate you need to adjust your prices (or your target market!)
Collocation refers to a sequence of words that commonly appear near each other. The most common types of collocations are unigram, bigrams and trigrams
Bigrams are pair of words that are likely to go together, like “Get started”, “Save time”, or “Decision making”.
Trigrams are a combination of three words, like “Within walking distance” or “Keep in touch”.
Identifying collocations — and counting them as one single word — improves the granularity of the text, allows a better understanding of its semantic structure and, in the end, leads to more accurate text mining results.
Concordance is used to recognize the particular context or instance in which a word or set of words appears. We all know that the human language can be ambiguous: the same word can be used in many different contexts. Analyzing the concordance of a word can help understand its exact meaning based on context.
For example, here are a few sentences extracted from a set of reviews including the word ‘work’:
Text extraction is a text analysis technique that extracts specific pieces of data from a text, like keywords, entity names, addresses, emails, etc. By using text extraction, companies can avoid all the hassle of sorting through their data manually to pull out key information. some of the main tasks of text extraction:
Name Entity Recognition
Most times, it can be useful to combine text extraction with text classification in the same analysis.
Keyword Extraction: keywords are the most relevant terms within a text and can be used to summarize its content. Utilizing a keyword extractor allows you to index data to be searched, summarize the content of a text or create tag clouds, among other things.
Name Entity Recognition
Named Entity Recognition allows you to identify and extract the names of companies, organizations or persons from a text.
Feature Extractionhelps identify specific characteristics of a product or service in a set of data. For example, if you are analyzing product descriptions, you could easily extract features like “colour”, “brand”, “model”, etc.
Text classification is the process of assigning categories (tags) to unstructured text data. This essential task of Natural Language Processing (NLP) makes it easy to organize and structure complex text, turning it into meaningful data. some of the most popular tasks of text classification are:
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