標題: Titlebook: Advances in Social Media Analysis; Mohamed Medhat Gaber,Mihaela Cocea,Ayse Goker Book 2015 Springer International Publishing Switzerland 2 [打印本頁] 作者: Taft 時間: 2025-3-21 18:02
書目名稱Advances in Social Media Analysis影響因子(影響力)
書目名稱Advances in Social Media Analysis影響因子(影響力)學科排名
書目名稱Advances in Social Media Analysis網絡公開度
書目名稱Advances in Social Media Analysis網絡公開度學科排名
書目名稱Advances in Social Media Analysis被引頻次
書目名稱Advances in Social Media Analysis被引頻次學科排名
書目名稱Advances in Social Media Analysis年度引用
書目名稱Advances in Social Media Analysis年度引用學科排名
書目名稱Advances in Social Media Analysis讀者反饋
書目名稱Advances in Social Media Analysis讀者反饋學科排名
作者: Isometric 時間: 2025-3-21 21:52 作者: 假裝是你 時間: 2025-3-22 00:49 作者: senile-dementia 時間: 2025-3-22 06:16 作者: Muffle 時間: 2025-3-22 12:32 作者: CON 時間: 2025-3-22 13:03 作者: 時代 時間: 2025-3-22 19:49 作者: 枕墊 時間: 2025-3-22 23:27
https://doi.org/10.1007/978-3-319-63504-0formation. We provide two main innovations in this work: first, the novel combination of text and multimedia opinion mining tools; and second, the adaptation of NLP tools for opinion mining specific to the problems of social media.作者: Indolent 時間: 2025-3-23 01:23
Entity-Based Opinion Mining from Text and Multimedia,formation. We provide two main innovations in this work: first, the novel combination of text and multimedia opinion mining tools; and second, the adaptation of NLP tools for opinion mining specific to the problems of social media.作者: 吸引人的花招 時間: 2025-3-23 08:08 作者: mucous-membrane 時間: 2025-3-23 10:40 作者: conduct 時間: 2025-3-23 14:49 作者: connoisseur 時間: 2025-3-23 21:01
https://doi.org/10.1007/978-3-319-18458-6Affective Computing; Computational Intelligence; Intelligent Techniques; Sentiment Analysis; Social Medi作者: prick-test 時間: 2025-3-23 22:37
978-3-319-35618-1Springer International Publishing Switzerland 2015作者: Carbon-Monoxide 時間: 2025-3-24 03:49 作者: Nonconformist 時間: 2025-3-24 08:10
Studies in Computational Intelligencehttp://image.papertrans.cn/a/image/149730.jpg作者: 印第安人 時間: 2025-3-24 11:01
Models in Environmental Planning, are much more diverse, and it is interesting to study how to define a more complete set of emotions and how to deduce these emotions from human-written messages. In this book chapter we argue that using Plutchik’s wheel of emotions model and a rule-based approach for emotion detection in text makes作者: 襲擊 時間: 2025-3-24 14:59
Computer Models for Speech Understanding,ly discover stories and eye-witness accounts. We present a technique that detects “bursts” of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic . approach and group?together bursty phrases that often appear in the same mes作者: 江湖騙子 時間: 2025-3-24 19:15 作者: 黃瓜 時間: 2025-3-25 02:08
https://doi.org/10.1007/978-3-319-63504-0nd centred on entity and event recognition. We examine a particular use case, which is to help archivists select material for inclusion in an archive of social media for preserving community memories, moving towards structured preservation around semantic categories. The textual approach we take is 作者: SOB 時間: 2025-3-25 06:46
https://doi.org/10.1007/978-3-319-63504-0s is of essence. However, this approach suffers from the semantic gap between the polarity with which a sentiment-bearing term appears in the text (i.e. contextual polarity) and its prior polarity captured by the lexicon. This is further exacerbated when mining is applied to social media. Here, we p作者: miniature 時間: 2025-3-25 07:52 作者: 陶醉 時間: 2025-3-25 14:33
https://doi.org/10.1007/978-3-319-63504-0r-supplied emotion labels (emoticons and smilies). Existing word segmentation tools proved unreliable; better accuracy was achieved using character-based features. Higher-order n-grams proved to be useful features. Accuracy varied according to label and emotion: while smilies are used more often, em作者: 創(chuàng)造性 時間: 2025-3-25 18:05 作者: 征稅 時間: 2025-3-25 23:08
Mining Newsworthy Topics from Social Media,ly discover stories and eye-witness accounts. We present a technique that detects “bursts” of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic . approach and group?together bursty phrases that often appear in the same mes作者: 結構 時間: 2025-3-26 01:23
Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis,h of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independ作者: Incommensurate 時間: 2025-3-26 07:51
Entity-Based Opinion Mining from Text and Multimedia,nd centred on entity and event recognition. We examine a particular use case, which is to help archivists select material for inclusion in an archive of social media for preserving community memories, moving towards structured preservation around semantic categories. The textual approach we take is 作者: 表主動 時間: 2025-3-26 10:49 作者: folliculitis 時間: 2025-3-26 15:55
Case-Studies in Mining User-Generated Reviews for Recommendation,chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from f作者: 殺子女者 時間: 2025-3-26 18:56
Predicting Emotion Labels for Chinese Microblog Texts,r-supplied emotion labels (emoticons and smilies). Existing word segmentation tools proved unreliable; better accuracy was achieved using character-based features. Higher-order n-grams proved to be useful features. Accuracy varied according to label and emotion: while smilies are used more often, em作者: Outshine 時間: 2025-3-26 23:40 作者: Fretful 時間: 2025-3-27 03:01
Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis,cts of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. We use a subset of hotel reviews from the TripAdvisor database [.] to作者: 束縛 時間: 2025-3-27 09:14 作者: GOAT 時間: 2025-3-27 10:50 作者: allude 時間: 2025-3-27 16:41
Computer Models for Speech Understanding,cs of news-related collections, and show that different strategies are needed to detect emerging topics within them. We show that our methods successfully detect a range of different topics for each event and can retrieve messages (for example, tweets) that represent each topic for the user.作者: 腫塊 時間: 2025-3-27 18:43 作者: nominal 時間: 2025-3-28 01:52 作者: placebo 時間: 2025-3-28 02:30
Embedding Sustainability into the Business Cores, so it can get safely and effectively transmitted and robust to outside forces. In this chapter, we discuss how to make sustainability an integral part of the business and ensure its perennity. The vectoring approach promoted throughout the book to develop and execute sustainability strategies not作者: 整潔 時間: 2025-3-28 06:27 作者: 騷擾 時間: 2025-3-28 13:02 作者: Arthr- 時間: 2025-3-28 17:32
