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Text Mining and Analysis | Text Mining and Analysis Assignment Help, Project Help and Homework Help

Text Analysis

During this we will covered the following topics:

  • 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


Intro to Text Mining

  • 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.




Text Analytics process

Text analytics, on the other hand, uses results from analyses performed by text mining models, to create graphs and all kinds of data visualizations.




Basic Methods

Word Frequency

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

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

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’:


Advanced Methods

1. Text Extraction

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:

  • Keyword Extraction

  • Name Entity Recognition

  • Feature Extraction

Most times, it can be useful to combine text extraction with text classification in the same analysis.


Text Extraction: Keyword Extraction

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.



Text Extraction: Name Entity Recognition

Named Entity Recognition allows you to identify and extract the names of companies, organizations or persons from a text.


Text Extraction: Feature Extraction

Feature Extraction helps 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.



2. Text Classification

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:

  • Topic Analysis

  • Language Detection

  • Intent Detection

  • Sentiment Analysis


Text Classification: Topic Analysis

Topic Analysis (also called topic detection, topic modelling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text’s topic or theme.

  • For example, a support ticket saying My Online Order Hasn’t Arrivedcan be classified as Shipping Issues”.


Text Classification: Language Detection

Language Detection allows you to classify a text based on its language. One of its most useful applications is automatically routing support tickets to the right geographically located team. Automating this task is quite simple and helps teams save valuable time.



Text Classification: Intent Detection

  • You could use a text classifier to recognize the intentions or the purpose behind a text automatically. This can be particularly useful when analyzing customer conversations.

  • For example, you could sift through different outbound sales email responses and identify the prospects which are interested in your product from the ones that are not, or the ones who want to unsubscribe.


Text Classification: Sentiment Analysis

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is used for many applications, especially in business intelligence. Some examples of applications for sentiment analysis include:

  • Analyzing the social media discussion around a certain topic

  • Evaluating survey responses

  • Determining whether product reviews are positive or negative



3. Text Analysis

Encompasses the processing and representation of text for analysis and learning tasks

- High-dimensionality

  • Every distinct term is a dimension

  • Green Eggs and Ham: A 50-D problem!

- Data is Un-structured



Text Analysis – Problem-solving Tasks

Parsing

  • Impose a structure on the unstructured/semi-structured text for downstream analysis

Search/Retrieval

  • Which documents have this word or phrase?

  • Which documents are about this topic or this entity?

Text-mining

  • "Understand" the content

  • Clustering, classification

Tasks are not an ordered list

  • Does not represent process

  • Set of tasks used appropriately depending on the problem addressed




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