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25-react-projects

25 Projects I Built to Learn React

feature-based-sentiment-analysis-in-python

react-bootstrap-website

React-Bootstrap Tutorial posted to YouTube - https://youtu.be/jgVkR5EKI68

sentiment-analysis

sentiment analysis of movie and product reviews using SentiWordNet and NLTK

sentiment-analysis-on-movie-reviews

Sentiment Analysis on Movie Reviews

sentiment-analyzer

Tweets Sentiment Analyzer

sentiment.analysis

Sentiment Analysis of IMDB Movie Reviews using word2vec and scikit-learn

sentimentanalysis

The code learns Support Vector Machines using 'linear' kernel to classify test data. Scikit-learn's LinearSVC provides a fast implementation for linear kernel SVMs. The features extracted from the text included the Tf-idf matrix, a commonly used method to convert text into a vetor space. Scikit-learn;s TfidVectorizer provides a complete and convenient set of functions for this. In addition to this, TruncatedSVD, another class in scikit-learn is used to approximate the feature vectors to a lower dimension (100~500 dimensions). Applying various combinations of the the different parameters (including the slack variable C in SVM, principal component size in SVD truncation and usage of monograms or bigrams in the feature extraction stage) a best estimator was selected based on

sentimentanalysis-moviereviews

The projects takes a number of movie reviews from http://www.cs.cornell.edu/people/pabo/movie-review-data/ (polarity dataset v2.0 and an independent dataset) and using SVM, Naive Bayes, KNN algorithms analysis the sentiment behind that review to being either positive or negative. The prediction is then compared to the actual sentiment behind the review and higher precision is targeted between all three algorithm. The research is then followed by a technical paper.