twitter sentiment analysis api

The trend of stock prices has always been hard to understand. But on the day of Facebook's IPO, Twitter magically predicted every trend in its stock price change. The social media monitoring platform Datasift monitors FacebookTwitter sentiment on the day of the IPO. That is to say, people publish a lot of comments about Facebook's IPO on Twitter, which may includ

2015. A major selling point of the API. Indeed, Metamind, a hot start-up, transformed from NLP to image analysis in 2015, due to the enormous opportunities behind its image analysis.Five, a breakthrough in speech analysis, video analysis followed byThe entire market likes to talk about multichannel analytics and user

The basis of text sentiment analysis is natural language processing, affective dictionary, machine learning method and so on. Here are some of the resources I've summed up.Dictionary resources:Sentiwordnet"Knowledge Network" Chinese versionChinese Affective polarity dictionary NTUSDEmotion Vocabulary Ontology DownloadNatural language processing tools and platforms:Institute of Social Computing and Informati

To undertake the former Wen springboot sentiment edify [email protected] annotation analysis, the recent project in contact with the use of JMX protocol framework, then on the basis of the previous article on how to integrate JMX Springboot Knowledge ReserveJmx:java Management Extension (Java Management application extension), this mechanism can easily manage and monitor running Java programs. Often us

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-7-3@author:guaguastd@name:tweet_lexical_diversity.py" ' I F __name__ = = ' __main__ ': # import login, see http://blog.csdn.net/guaguastd/article/details/31706155 from Login im Port Twitter_login # Get the twitter access API Twitter_api = Twitter_login () # import tweet from Tweet im Port extract_tweet_entities # import Search from search import sea

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-7-9@author:guaguastd@name:entities_frequency_map.py" if _ _name__ = = ' __main__ ': # import Counter from collections Import Counter # import visualize from visualize Import Visualize_frequency_map # pip install prettytable # from prettytable import prettytable # import Searc H from search import Search_for_tweet # import login, see http://blog.csdn.net/guaguastd/article/details/317 06155 from login Import Twitter_login # Get the