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Sentiment analysis is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions expressed. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The applications of sentiment analysis are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organizations across the world.
Why: Problem statement
Most of the good doctors are invisible to the public. Patients are seeking for the good and familiar doctors.
How: Solution description
In order to make the good doctors familiar, I am going to analyze the feedback of the patients. Here, I used sentiment analysis to review patient feedback for a select group of doctors from overall Pondicherry. I used Python and Jupyter Notebook to develop our system, relying on Scikit-Learn for the machine learning components.
Bag of words:
The classifiers and learning algorithms can not directly process the text documents in their original form, as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length. Therefore, during the preprocessing step, the texts are converted to a more manageable representation.
One common approach for extracting features from the text is to use the bag of words model: a model where for each document, the patient feedback in our case, the presence of words is taken into consideration, but the order in which they occur is ignored.
Specifically, for each term in our dataset, we will calculate a measure called Term Frequency-Inverse Document Frequency (tf-idf). We will use sklearn.feature_extraction.text.TfidfVectorizer to calculate a tf-idfvector for each of patient feedback. Finally, we can even reduce the weightage of more common words which occurs in all document.
Now, each of 570 patient feedback is represented by 4 features, representing the tf-idf score for different unigrams and bigrams.
Scikit-learn has a high-level component which will create feature vectors for us ‘CountVectorizer’. Then, I split the total data into train and test data using train_test_split method. Finally, it was split into 456 items in training data, 114 in test data.
A stop word is a commonly used word that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
We would not want these words taking up space in our database, or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to stop words. NLTK(Natural Language Toolkit) in python has a list of stopwords stored in the nltk_data directory
Machine Learning Models:
We are now ready to experiment with different machine learning models, evaluate their accuracy and find the source of any potential issues.
We will benchmark the following three models:
Logistic Regression: 0.97
Bernoulli NB: 0.93
Continue with our best model (Logistic Regression), we are going to look at the confusion matrix, and show the similarity between predicted and actual labels. The vast majority of the predictions end up on the diagonal (predicted label = actual label), where we want them to be.
This is the result of testing with sample feedback from the patients.
How is it different from competition
I used Logistic Regression which gives more accuracy than any other algorithms.
Who are your customers
Useful to the patients who are suffering from any type of disorder.
Data Scientists can use this for study and literature survey.