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affectivetweets

A WEKA package for analyzing emotion and sentiment of tweets.

amazon-fake-review-detection

arima-and-seasonal-arima

asl-ml-immersion

This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

fakereviewdataset

flight-delay-prediction

linkedin-python-content-analytics

Web Scraping and Analyzing Performance of Your Content on LinkedIn.

real-time-sentiment-tracking-on-twitter-for-brand-improvement-and-trend-recognition

A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)

reference-codes

scrape-amazon-reviews

semantic_analysis_using_nlp-manning-project

syntactic-processing

time-series-prediction

titanic-dataset-kaggle

yelp-fake-review-detection

Online reviews play a pivotal role in helping people purchase products eventually influencing the business verdicts. Online reviews have led to fake review writing, which can either be paid human writers or machine generated deceptive reviews with the aim to influence future customers opinion. In this project we have tried to tackle this problem with the help of a classifier that processes the review text and user’s behavioral pattern as input and predicts whether the review is genuine or not. The learning algorithms we experimented include logistic regression, multinomial Naive Bayes, K-Nearest Neighbour (KNN), Random Forest classifier, Convolutional Neural Network (CNN) and CNN with Long short term memory (LSTM). From the results of our experiment we can see that Logistic Regression and KNN performed better with approximately 60-64% accuracy and Naive Bayes and Random Forest Classifier (RFC) does not work well with our dataset with only 50% accuracy and CNN-LSTM does not give a very high accuracy (21%) i.e. but has a good recall of 0.82.