TranThuy274's repositories
appcomposer1
appcomposer
applogin
App Login
apptemplate
apptemplate
PHP
LPHP1901e
SimpleCart
Simple cart
simplephpcast
Giỏ hàng đơn giản với Php và mysql
Twitter-Trends
Twitter Trends is a web-based application that automatically detects and analyzes emerging topics in real time through hashtags and user mentions in tweets. Twitter being the major microblogging service is a reliable source for trends detection. The project involved extracting live streaming tweets, processing them to find top hashtags and user mentions and displaying details for each trending topic using trends graph, live tweets and summary of related articles. It also included Topic Modelling and Entity Categorization to classify the tweets and extract valuable information about its contents and find similar tweets and related articles and URLs. A trending topic is represented as a word cloud created from set of keywords (hashtags or user mentions) that belong to that topic. Thus this application provides the required information to get an overhaul of the topics which are trending at that particular time. This data can be used to support social analysis, finance, marketing or news tracking.
TwitterDataAnalysis
In this project we did sentimental analysis on data collected from the social media, Twitter and predicted the current trend. The data can be tweets, quoted tweets and the favorites for a tweet (the number of times a tweet has been liked). Data was collected for a pair of keywords using the Twitter Search API. The collected tweets are then classified as positive, negative, neutral or junk based on the sentimental analysis of the text in the tweet/quoted tweet (favorites are considered as positive). Based on this classification it is possible to predict which among the pair of keywords is more popular. The prediction is under the assumption that more positive and neutral responses are there for a keyword, more trending it is with the public. An Android app was created to display data analysis results for a pair of keywords The accuracy of prediction was examined by predicting the outcome of November 5th Governor Elections in New Jersey using keywords Barbara Buono and Chris Christie.