Table of Content
Analyzing unstructured Text data by generating structured data in key-value pair form. Deriving insights from the extracted keywords by arranging the extracted keywords in a plain space with font sizes varying based on their frequency is called WordCloud.
Collect the text data / Extract data from sources.
Pre-Process the Data
- Case - uppercase / lowercase / proper case
- Punctuations & special symbols (‘%’, ‘!’, ‘&’, etc.)
- Filler words, connectors, pronouns (‘all’, ‘for’, ‘of’, ‘my’, ‘to’, etc.)
- Extra White spaces
- Custom words
- Tokenization - Tokenization refers to the process of splitting a sentence into its constituent words
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Document Term Matrix / Term Document Matrix
Documents arranged in rows and Terms arranged in columns is called as DTM and transpose of DTM is TDM.
Positive Word Cloud - words present in positive dictionary.
Negative Word Cloud - words present in negative dictionary.
Bigram - two words repeated together - gives better context of the content.
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Natural Language Processing (NLP)
Text Analytics is the method of extracting meaningful insights and answering questions from text data.
Natural Language Understanding(NLU)
A process by which an inanimate object (not alive - machines, systems, robots) with computing power is able to comprehend spoken language.
Example: Humans talk to robot
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Natural Language Generation (NLG)
A process by which an inanimate object (not alive - machines, systems, robots) with computing power is able to manifest its thoughts in a language that humans are able to understand.
Example: Robot responds to human queries
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Topic Modeling Algorithms
LSA/LSI (Latent Semantic Analysis/Latent Semantic Indexing)
Reducing dimension for classification. LSA assumes that the words will occur in similar pieces of text if they have similar meaning.
LDA (Latent Dirichlet Allocation)
A topic modelling method that generates topics based on words/expression frequency from documents.
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Process of producing concise version of text by retaining all the important information.
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