Detecting-sentiments-of-image-quote (HackerEarth's ML Challenge Love_is-Love)

A HackerEarth’s ML Challenge based on Detecting sentiments of image quote using unsupervised Machine Learning techniques and knowledge of OCR and NLP to build a model that analyzes sentiments of a quote and classifies them into positive, negative, or random.

My Approach

• Used glob to extract all the paths for the images
• In one approach i used cv2 and pytesseract library (images needs to be preprocessed using cv2 library) to read the text from the .jpg files.
• And in other i used easyocr library (gave better results) to read the text from the .jpg files.
• Removed \n and contraction then applied some spelling correction and created Sentiment scores using SentimentIntensityAnalyzer & TextBlob Sentiment polarity score
• Removed Non alphabets, Stopwords and applied lemmatization
• Created TFIDF vectors for the text
• Created vector represenation for each comment
• Stacked all these features i.e. Sentiment scores, TFIDF Vectors, Comment Vectors And used these as features to cluster the comments in 3 groups using Kmeans.
• Further by inspection detected the comment types in the cluster and mapped Random, Negative, Positive accordingly.