Non Negative Matrix Factorization
Key Textual Strategies
Text Mining Techniques
Analyzing Product Feedback Using Text
Understanding Brand Perception through Text Data
Text Mining Approaches
Analyzing Product Reviews through Text Analytics
Techniques for Analyzing Textual Data Efficiently
Text Analysis Techniques for Market Research 
Lexicon-based approaches: These involve using predefined lists of words and phrases categorized by sentiment (positive,
negative, neutral)
...Non-negative
Matrix Factorization (NMF) A linear algebra technique that decomposes the document-term matrix into topic distributions
...
Key Textual Strategies Description Lexicon-Based Uses predefined lists of words associated with positive or
negative sentiments
...Non-negative
Matrix Factorization (NMF): Factorizes the document-term matrix into two lower-dimensional matrices to identify topics
...
Text Mining Techniques Sentiment Analysis Determining the sentiment expressed in a text, whether positive,
negative, or neutral
...Non-Negative
Matrix Factorization (NMF): Factorizes the document-term matrix into topics and their associations
...
Analyzing Product Feedback Using Text Sentiment Analysis: It allows businesses to gauge customer sentiment, determining whether feedback is positive,
negative, or neutral
...Non-negative
Matrix Factorization (NMF): A group of algorithms in multivariate analysis and linear algebra used to factorize data matrices
...
Understanding Brand Perception through Text Data Increased customer loyalty Higher market share Enhanced brand equity Improved customer satisfaction Conversely,
negative brand perception can result in: Decreased sales Damage to brand reputation Loss of customer trust Increased marketing costs to rebuild brand image
...Common techniques include: Latent Dirichlet Allocation (LDA)
Non-negative
Matrix Factorization (NMF) Latent Semantic Analysis (LSA) 3
...
Text Mining Approaches sentiment analysis include: Lexicon-Based Approaches: Utilizing predefined lists of words associated with positive or
negative sentiments
...Non-Negative
Matrix Factorization (NMF) A linear algebra approach that factorizes the document-term matrix into lower-dimensional matrices
...
Analyzing Product Reviews through Text Analytics Sentiment Analysis Sentiment analysis involves classifying the sentiment expressed in reviews as positive,
negative, or neutral
...Common methods include: Latent Dirichlet Allocation (LDA)
Non-Negative
Matrix Factorization (NMF) Tools for Text Analytics Several tools and software are available for performing text analytics on product reviews: Tool Description Python
...
Techniques for Analyzing Textual Data Efficiently Lexicon-Based Approach: Utilizes predefined lists of words associated with positive or
negative sentiments
...Non-Negative
Matrix Factorization (NMF): A linear algebra technique for decomposing matrices into non-negative factors
...
Key Textual Insights Common Tools Sentiment Analysis Determines the sentiment (positive,
negative, neutral) expressed in a piece of text
...Latent Dirichlet Allocation (LDA),
Non-negative
Matrix Factorization (NMF) Text Classification Categorizes text into predefined classes or labels
...
Understanding Consumer Behavior with Text Analytics Percentage Positive 65% Neutral 20%
Negative 15% 2
...Common techniques include: Latent Dirichlet Allocation (LDA)
Non-negative
Matrix Factorization (NMF) For instance, a retail company may analyze customer feedback to identify common topics such as product quality, pricing, and customer service
...
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