ERROR: MethodError: no method matching DocumentTermMatrix(::Vector{String})

I am trying to train a basic SVM model for multiclass text classification in Julia. My dataset has around 75K rows and 2 columns (text and label). The context of the dataset is the abstracts of scientific papers gathered from PubMed. I have 10 labels in the dataset.

The dataset looks like this:

I keep receiving two different Method errors. The starting one is:

ERROR: MethodError: no method matching DocumentTermMatrix(::Vector{String})

I have tried:

convert(Array,data[:,:text])

and also:

convert(Matrix,data[:,:text])

Array conversion gives the same error, and matrix conversion gives:

ERROR: MethodError: no method matching (Matrix)(::Vector{String})

My code is:

using DataFrames, CSV, StatsBase,Printf, LIBSVM, TextAnalysis, Random

function ReadData(data)
    df = CSV.read(data, DataFrame)
    return df
end

function splitdf(df, pct)
    @assert 0 <= pct <= 1
    ids = collect(axes(df, 1))
    shuffle!(ids)
    sel = ids .<= nrow(df) .* pct
    return view(df,sel, :), view(df, .!sel, :)
end

function Feature_Extract(data)
    Text =   convert(Array,data[:,:text])
    m = DocumentTermMatrix(Text)
    X = tfidf(m)
    return X
    
end

function Classify(data)
    data = ReadData(data)
    
    train, test = splitdf(data, 0.5)  
    ytrain = train.label
    ytest  = test.label

    Xtrain = Feature_Extract(train)
    Xtest = Feature_Extract(test)
    
    model = svmtrain(Xtrain, ytrain)
    ŷ, decision_values = svmpredict(model, Xtest);

    @printf "Accuracy: %.2f%%\n" mean(ŷ .== ytest) * 100
end

data = "data/composite_data.csv"

@time Classify(data)

I appreciate your help to solve this problem.

EDIT:

I have managed to get the corpus but now facing DimensionMismatch Error:

 using DataFrames, CSV, StatsBase,Printf, LIBSVM, TextAnalysis, Random

function ReadData(data)
    df = CSV.read(data, DataFrame)
    #count = countmap(df.label)
    #println(count)
    #amt,lesslabel = findmin(count)
    #println(amt, lesslabel)
    #println(first(df,5))
    return df
end

function splitdf(df, pct)
    @assert 0 <= pct <= 1
    ids = collect(axes(df, 1))
    shuffle!(ids)
    sel = ids .<= nrow(df) .* pct
    return view(df,sel, :), view(df, .!sel, :)
end

function Feature_Extract(data)
    crps = Corpus(StringDocument.(data.text))
    update_lexicon!(crps)
    m = DocumentTermMatrix(crps)
    X = tf_idf(m)
    return X
    
end

function Classify(data)
    data = ReadData(data)
    #println(labels)
    #println(first(instances))
    
    train, test = splitdf(data, 0.5)  
    ytrain = train.label
    ytest  = test.label

    Xtrain = Feature_Extract(train)
    Xtest = Feature_Extract(test)
    
    model = svmtrain(Xtrain, ytrain)
    ŷ, decision_values = svmpredict(model, Xtest);

    @printf "Accuracy: %.2f%%\n" mean(ŷ .== ytest) * 100
end

data = "data/composite_data.csv"

@time Classify(data)

Error:

ERROR: DimensionMismatch("Size of second dimension of training instance\n matrix (247317) does not match length of\n labels (38263)")