Sentimental Analysis using SVM

Hi,

I am a newbie to Matlab and coding.

I am trying to do sentimental analysis with tweet text data extracted from twitter API using SVM.

I have managed to preprocess the text. But I am stuck there.

I need to extract features and want to see the word frequency and user's perception towards target companies and so forth. Lastly, I need to evaluate the performance too.

how should I extract features and train and test the data?

Thank you.

Answer 1

[Drew]

You can find example code of sentiment analysis using SVMs in this post: https://www.mathworks.com/matlabcentral/answers/1945859-hybrid-method-to-sentiment-analysis-column-number-error

You can also look at these doc pages:

• https://www.mathworks.com/help/textanalytics/ug/train-a-sentiment-classifier.html
• https://www.mathworks.com/discovery/sentiment-analysis.html

If you find this answer helpful, please remember to "accept" the answer.

[Sanguk]

Hi,

I followed the step in the link, but I get errors saying "Error using horzcat

Dimensions of arrays being concatenated are not consistent.

Error in reun (line 44)

XTrain = [XTrain, sentimentScoresTrain];"

how should I fix it?

filename = "sentiment_irrelevantdrop_all"; 
data = readtable(filename,'TextType','string');
data.sentiment = categorical(data.sentiment);

% Split dataset into training and test sets using holdout
cvp = cvpartition(data.sentiment, 'Holdout', 0.1);
dataTrain = data(cvp.training, :);
dataTest = data(cvp.test, :);

% Extract review text and sentiment labels from training and test set 
textDataTrain = dataTrain.text;
textDataTest = dataTest.text;
YTrain = dataTrain.sentiment;
YTest = dataTest.sentiment;

% Preprocess training set
documents = preprocessText(textDataTrain);

% Create bag of words and remove infrequent words
bag = bagOfWords(documents);
bag = removeInfrequentWords(bag,2);
[bag,idx] = removeEmptyDocuments(bag);
YTrain(idx) = [];

% Encode training set using bag of words
XTrain = bag.Counts;

% Train SVM classifier
mdl = fitcecoc(XTrain, YTrain, "Learners", "linear");  

% Preprocess test set
documentsTest = preprocessText(textDataTest);
documentsTrain = preprocessText(textDataTrain);

% Encode test set using bag of words
XTest = encode(bag, documentsTest);

% Compute sentiment scores for training and test sets using VADER
sentimentScoresTrain = vaderSentimentScores(documentsTrain);
sentimentScoresTest = vaderSentimentScores(documentsTest);

% Concatenate sentiment scores with bag of words features
XTrain = [XTrain, sentimentScoresTrain];
XTest = [XTest, sentimentScoresTest];

% Build new svm model using both bag-of-words and vader sentiment scores as
% features
mdl2 = fitcecoc(XTrain, YTrain, "Learners", "linear");  

% Predict sentiment labels for test set
YPred = predict(mdl, XTest);

% Evaluate performance
accuracy = sum(YPred == YTest) / numel(YTest);
fprintf("Accuracy: %.2f%%\n", accuracy * 100);

confusion = confusionmat(YTest, YPred);
truePositive = confusion(1, 1);
falsePositive = confusion(2, 1);
trueNegative = confusion(2, 2);
falseNegative = confusion(1, 2);

% Compute precision, recall, and F-measure
precision = truePositive / (truePositive + falsePositive);
recall = truePositive / (truePositive + falseNegative);
fMeasure = 2 * precision * recall / (precision + recall);

% Compute accuracy 
accuracy2 = (truePositive + trueNegative) / numel(YTest);

% Display results 
disp(['True positive: ' num2str(truePositive)]);
disp(['False positive: ' num2str(falsePositive)]);
disp(['True negative: ' num2str(trueNegative)]);
disp(['False negative: ' num2str(falseNegative)]);
disp(['Precision: ' num2str(precision)]);
disp(['Recall: ' num2str(recall)]);
disp(['F-measure: ' num2str(fMeasure)]);

function documents = preprocessText(textData)
documents = tokenizedDocument(textData);
documents = addPartOfSpeechDetails(documents);
documents = removeStopWords(documents);
documents = erasePunctuation(documents);
documents = removeShortWords(documents,2);
documents = removeLongWords(documents,15);
end

Thank you