Sentiment analysis of top colleges in India using Twitter data

Sentiment Analysis: A Review

“…Nehal Mamgain, Ekta Mehta, Ankush Mittal, Gaurav Bhatt [6] have performed similar work that shows a comparison between the results obtained by exploiting the following machine learning algorithms: Naïve Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. They defined future work as to aim to classify using smiley or emoticons.…”

Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python

“…The work of [5] is a bit different as they don't focus on a particular topic or event but propose to find trending topics in a region. The features extracted are divided in two categories: Common Features and Tweet Specific Features.…”

“…The tweets are collected using Twitter's streaming API [1], [3], or any other mining tool (for example WEKA [2]), for the desired time period of analysis. The format of the retrieved text is converted as per convenience (for example JSON in case of [3], [5]). …”

“…[3] uses WEKA machine learning package for this purpose, which checks each word from the text against a dictionary ([3], [5]). …”

“…The number of hidden layers is very important metric for the performance of the model. There are two steps of working of MLP NN-feed forward propagation, involving learning features from feed forward propagation algorithm and back propagation, for cost function [5], [10].…”