Latent semantic analysis (LSA) model
A latent semantic analysis (LSA) model discovers relationships between documents and the words that they contain. An LSA model is a dimensionality reduction tool useful for running low-dimensional statistical models on high-dimensional word counts. If the model was fit using a bag-of-n-grams model, then the software treats the n-grams as individual words.
Create an LSA model using the
NumComponents— Number of components
Number of components, specified as a nonnegative integer. The number of
components is the dimensionality of the result vectors. Changing the value
NumComponents changes the length of the resulting
vectors, without influencing the initial values. You can only set
NumComponents to be less than or equal to the
number of components used to fit the LSA model.
FeatureStrengthExponent— Exponent scaling feature component strengths
Exponent scaling feature component strengths for the
properties, and the
transform function, specified as a
nonnegative scalar. The LSA model scales the properties by their singular
values (feature strengths), with an exponent of
ComponentWeights— Component weights
Component weights, specified as a numeric vector. The component weights of
an LSA model are the singular values, squared.
ComponentWeights is a
NumComponents vector where the
jth entry corresponds to the weight of component
j. The components are ordered by decreasing weights.
You can use the weights to estimate the importance of components.
DocumentScores— Score vectors per input document
Score vectors per input document, specified as a matrix. The document
scores of an LSA model are the score vectors in lower dimensional space of
each document used to fit the LSA model.
is a D-by-
NumComponents matrix where
D is the number of documents used to fit the LSA
model. The (i,j)th entry of
DocumentScores corresponds to the score of
component j in document i.
WordScores— Word scores per component
Word scores per component, specified as a matrix. The word scores of an
LSA model are the scores of each word in each component of the LSA model.
WordScores is a
NumComponents matrix where
V is the number of words in
Vocabulary. The (v,j)th entry of
WordScores corresponds to the score of word
v in component j.
Vocabulary— Unique words in model
Unique words in the model, specified as a string vector.
|Transform documents into lower-dimensional space|
Fit a Latent Semantic Analysis model to a collection of documents.
Load the example data. The file
sonnetsPreprocessed.txt contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from
sonnetsPreprocessed.txt, split the text into documents at newline characters, and then tokenize the documents.
filename = "sonnetsPreprocessed.txt"; str = extractFileText(filename); textData = split(str,newline); documents = tokenizedDocument(textData);
Create a bag-of-words model using
bag = bagOfWords(documents)
bag = bagOfWords with properties: Counts: [154x3092 double] Vocabulary: ["fairest" "creatures" "desire" ... ] NumWords: 3092 NumDocuments: 154
Fit an LSA model with 20 components.
numComponents = 20; mdl = fitlsa(bag,numComponents)
mdl = lsaModel with properties: NumComponents: 20 ComponentWeights: [2.7866e+03 515.5889 443.6428 316.4191 ... ] DocumentScores: [154x20 double] WordScores: [3092x20 double] Vocabulary: ["fairest" "creatures" "desire" ... ] FeatureStrengthExponent: 2
Transform new documents into lower dimensional space using the LSA model.
newDocuments = tokenizedDocument([ "what's in a name? a rose by any other name would smell as sweet." "if music be the food of love, play on."]); dscores = transform(mdl,newDocuments)
dscores = 2×20 0.1338 0.1623 0.1680 -0.0541 -0.2464 -0.0134 -0.2604 -0.0205 0.1127 0.0627 0.3311 -0.2327 0.1689 -0.2695 0.0228 0.1241 0.1198 0.2535 -0.0607 0.0305 0.2547 0.5576 -0.0095 0.5660 -0.0643 -0.1236 0.0082 0.0522 -0.0690 -0.0330 0.0385 0.0803 -0.0373 0.0384 -0.0005 0.1943 0.0207 0.0278 0.0001 -0.0469
Create a bag-of-words model from some text data.
str = [ "I enjoy ham, eggs and bacon for breakfast." "I sometimes skip breakfast." "I eat eggs and ham for dinner." ]; documents = tokenizedDocument(str); bag = bagOfWords(documents);
Fit an LSA model with two components. Set the feature strength exponent to 0.5.
numComponents = 2; exponent = 0.5; mdl = fitlsa(bag,numComponents, ... 'FeatureStrengthExponent',exponent)
mdl = lsaModel with properties: NumComponents: 2 ComponentWeights: [16.2268 4.0000] DocumentScores: [3x2 double] WordScores: [14x2 double] Vocabulary: ["I" "enjoy" "ham" "," ... ] FeatureStrengthExponent: 0.5000
Calculate the cosine distance between the documents score vectors using
pdist. View the distances in a matrix
D(i,j) denotes the distance between document
dscores = mdl.DocumentScores; distances = pdist(dscores,'cosine'); D = squareform(distances)
D = 3×3 0 0.6244 0.1489 0.6244 0 1.1670 0.1489 1.1670 0
Visualize the similarity between documents by plotting the document score vectors in a compass plot.
figure compass(dscores(1,1),dscores(1,2),'red') hold on compass(dscores(2,1),dscores(2,2),'green') compass(dscores(3,1),dscores(3,2),'blue') hold off title("Document Scores") legend(["Document 1" "Document 2" "Document 3"],'Location','bestoutside')