在R上使用Facebook进行分析 使用R编程语言通过Graph API对从Facebook获得的社交数据进行数据分析和自然语言处理。 该项目根据人的社交活动以及语言或单词使用情况绘制图表。 还进行了基于极性和情感的情感分析，并绘制了图表。

☆ 资源说明：☆ [Packt Publishing] 社交数据可视化 (HTML5 & JavaScript 实现) (英文版) [Packt Publishing] Social Data Visualization with HTML5 and JavaScript (E-Book) ☆ 图书概要：☆ Overview Learn how to use JavaScript to create compelling visualizations of social data Use the d3 library to create impressive SVGs Master OAuth and how to authenticate with social media sites ☆ 出版信息：☆ [作者信息] Simon Timms [出版机构] Packt Publishing [出版日期] 2013年09月25日 [图书页数] 104页 [图书语言] 英语 [图书格式] PDF 格式

心理健康和社交媒体一直是密切相关的研究领域。 在这项研究中，提出了一种新模型 AD 预测模型，用于实时推文中的焦虑抑郁预测。 这种混合性焦虑抑郁症主要与不稳定的思维过程、烦躁和失眠有关。 基于语言提示和用户发布模式，使用 5 元组向量定义特征集。

70+Wgg抓取Facebook,twitter,pinterest,linkedin,google+社交数据

# -*- coding: utf-8 -*- import sys import nltk import json # Load in output from blogs_and_nlp__get_feed.py BLOG_DATA = sys.argv[1] blog_data = json.loads(open(BLOG_DATA).read()) for post in blog_data: sentences = nltk.tokenize.sent_tokenize(post['content']) tokens = [nltk.tokenize.word_tokenize(s) for s in sentences] pos_tagged_tokens = [nltk.pos_tag(t) for t in tokens] # Flatten the list since we're not using sentence structure # and sentences are guaranteed to be separated by a special # POS tuple such as ('.', '.') pos_tagged_tokens = [token for sent in pos_tagged_tokens for token in sent] all_entity_chunks = [] previous_pos = None current_entity_chunk = [] for (token, pos) in pos_tagged_tokens: if pos == previous_pos and pos.startswith('NN'): current_entity_chunk.append(token) elif pos.startswith('NN'): if current_entity_chunk != []: # Note that current_entity_chunk could be a duplicate when appended, # so frequency analysis again becomes a consideration all_entity_chunks.append((' '.join(current_entity_chunk), pos)) current_entity_chunk = [token] previous_pos = pos # Store the chunks as an index for the document # and account for frequency while we're at it... post['entities'] = {} for c in all_entity_chunks: post['entities'][c] = post['entities'].get(c, 0) + 1 # For example, we could display just the title-cased entities print post['title'] print '-' * len(post['title']) proper_nouns = [] for (entity, pos) in post['entities']: if entity.istitle(): print '\t%s (%s)' % (entity, post['entities'][(entity, pos)]) print

社交网站数据挖掘与分析[Mining the Social Web](英文完整+中文样章+源代码