1. The document discusses the history and applications of sentiment analysis, which is a technique used to categorize texts as positive or negative based on opinions and emotions expressed.
2. Machine learning (ML) techniques have increasingly been used for sentiment analysis since the 2000s, allowing for automated classification of texts like product reviews and social media posts.
3. ML approaches to sentiment analysis include classifying texts based on sentiment-bearing features, assessing sentiments towards specific aspects or topics, and automatically labeling text snippets as positive or negative based on linguistic patterns.
Presentation doc for CSS Nite in Ginza, Vil.36 on June 18th., 2009.
"The Past, Present and the Future of Information Architect"
Theme: Explain the history and the evolution of Information Architect as a role and as a profession.
Archives: http://cssnite.jp/ginza/vol36/
Follow up: http://cssnite.jp/archives/post_1558.html
# both in Japanese
Presentation doc for CSS Nite in Ginza, Vil.36 on June 18th., 2009.
"The Past, Present and the Future of Information Architect"
Theme: Explain the history and the evolution of Information Architect as a role and as a profession.
Archives: http://cssnite.jp/ginza/vol36/
Follow up: http://cssnite.jp/archives/post_1558.html
# both in Japanese
RSGT2021 Bilingual cross-cultural discussion 日本人と外国人のディスカッション: How to acceler...Rochelle Kopp
Presentation and discussion notes -- presentation at Regional Scrum Gathering Tokyo 2021 by Rochelle Kopp of Japanese Intercultural Consulting and Tatsuya Kinugawa of Rakuten
Describes about Eco Style, the first IT system in the world to help decrease the amount of Co2 comming out from a company. Built by Beat Communication.
RSGT2021 Bilingual cross-cultural discussion 日本人と外国人のディスカッション: How to acceler...Rochelle Kopp
Presentation and discussion notes -- presentation at Regional Scrum Gathering Tokyo 2021 by Rochelle Kopp of Japanese Intercultural Consulting and Tatsuya Kinugawa of Rakuten
Describes about Eco Style, the first IT system in the world to help decrease the amount of Co2 comming out from a company. Built by Beat Communication.
文献紹介:Semantic-based information retrieval in support of concept design.Shin Sano
A new method of image retrieval using semantic technology to best support concept designers to obtain design inspirations.
Keywords: Concept design, Creativity, Inspiration, Image retrieval, Semantic technology, Semantics
コンセプト・デザイナーがデザインを発想する際に使う言葉と視覚イメージを検索する
各種方法を検討し、セマンティック検索を可能にするデザイナー向けオントロジーを含むシステムを提案、効果を評価。EU産学連携コンソーシアム
這是2009/03/28 受邀在台北大安扶輪青年服務團分享的英國旅遊與生活點滴。
有興趣的可至以下連結下載:
http://www.fileqube.com/wf/183805/1399747
相關文章:http://blog.roodo.com/vincent_y_t_lu
This slide is used to present my personal perspective on the culture and beauty of the United Kingdom. It has been used once in a regular meeting of Rotaract Club of Taipei TA-AN on 28 March 2009.
For those interested, the complete file can be download here: http://www.fileqube.com/wf/183805/1399747
Relevant article can be found on my blog: http://blog.roodo.com/vincent_y_t_lu
p.s. 因為在slideshare網站上下載後的檔案無法變成單一的powerpoint檔。而且直接在這邊瀏覽許多投影片中的照片也只能看到疊在最上面的那一張,會錯失很多精彩的相片,所以我把完整檔移到fileQube上,這邊並不提供下載。
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
7. 大きな流れ
• 1990年代
– 形容詞の肯定/否定分類(Hatzivassiloglou and McKeown, 1997)
e.g., adequate → 肯定的, troublesome → 否定的
• 2000年代
– レビュー文書の肯定/否定分類(Turney, 2002; Pang et al., 2002)
– これ以降,評判分析が爆発的に流行
– 評判分析を専門に扱う国際ワークショップ
e.g., AAAI spring symposium 2004, ACL workshop 2006
– タスクが複雑化,詳細化しながら現在にいたる
8. MLが使われる局面(1/2)
1. 評判情報を観点とした文書分類
評判が記述された文書 肯定 or 否定
(レビューなど)
2. 属性(aspect)に着目した評判要約 or 抽出
あるレストランの評判の要約例
(Titov and McDonald, 2008)
Aspect Score Mention
Food ☆☆☆☆ “Best fish in the city”, “Excellent appetizer”
Decor ☆☆☆ “Cozy with an old world feel”, “Too dark”
レビュー集合
Service ☆ “Our waitress was rude”, “Awful service”
14. (Pang et al., 2002)以降
A) 分類カテゴリの詳細化
B) 評判箇所の検出
C) 肯否定が混在する文書の扱い
15. A) 分類カテゴリの詳細化
• 肯定/否定より細かい粒度の分類
– 2値分類 (Pang et al., 2002)の素直な拡張
• 問題設定
– 肯定/否定/中立の3値分類 (Koppel and Scheler, 2006)
– 4 or 5段階のスコア付け(Pang and Lee, 2005; Okanohara
and Tsujii, 2007)
• 新しい設定に適したアルゴリズム
– SVR (Vapnik, 1995; Smola and Scholkopf, 1998)
– Metric labeling (Kleinberg and Tardos, 2002)
16. 分類 VS. 回帰
• 実験例 (Okanohara and Tsujii, 2007)
– 本のレビューを5段階でスコア付け
– Mean square error で評価
Method Mean square error(corpus A, corpus B)
Pairwise SVM 1.32 2.13
SVR 0.94 1.38
改善が見られる
17. B) 評判箇所の検出
客観的事実の記述(レビューの場
合,主に本や映画のあらすじ)
“Harry Potter (本)” のレビュー の例(Okanohara and Tsujii, 2007)
It is a fantasy fairytale, sometimes linked to Cinderella, about
a young orphaned boy transported into a world of magic and
sorcery. Harry Potter finds himself at a school for wizards,
where his reputation precedes him, and soon becomes
embroiled in a classic battle of a good versus evil. …. The
pages shimmer with creativity, and although an easy read for
adults, I would recommend it heartily to anyone that enjoys
escaping the real world for an hour or three.
主観的な記述(= 評判)
18. 段階的処理
客観+主観 主観のみ (Pang et al., 2002)
文1 文1
文2
肯定 or 否定
文3
文4 文4
• 主観/客観の2値分類
• BOW素性 + ナイーブベイズ or SVMs etc.
• 客観的事実のexample
– 本,映画のあらすじ,新聞記事
19. ひと工夫
• 近接性の利用(Pang and Lee, 2004)
– 近くに出現する2文の主観/客観は一致しやすい
• モデルの“イメージ”
– 確率モデルを作っているわけではない
主/客 主/客 主/客 主/客 主/客 主/客
…… ……
文1 文2 文3 文1 文2 文3
Before After
23. C) 肯否定が混在する文書の扱い
• 文書とは別に,文ごとにも肯定と否定を推定したい
This is the first Mp3 player that I have used … I
thought it sounded great… After only a few weeks,
it started having trouble with the earphone connection
… I won’t be buying another.
• 文書レベルの肯定/否定が有効な手掛かり
My 11 year old daughter has also been using it and it is
a lot harder than it looks.
実はfitness 器具に関する記述なので hard なのは良いこと
29. 評判要約
• 特定製品に関する評判情報を構造化
– いわゆる情報抽出に近く,評判抽出とも言われる
• 1つの例(Titov and McDonald, 2008)
– 属性(aspect)ごとに評判をまとめるのが主流
あるレストランに関する評判の要約
Aspect Score Mention
Food ☆☆☆☆ “Best fish in the city”, “Excellent appetizer”
Decor ☆☆☆ “Cozy with an old world feel”, “Too dark”
レストランのレビュー
Service ☆ “Our waitress was rude”, “Awful service”
30. 評判の要約に関する研究
• Rule (Nasukawa and Yi, 2003; Kanayama et al., 2004)
• Pattern mining (Hu and Liu, 2004; Liu et al., 2005)
• Clustering + visualization (Gamon et al., 2005)
• Log-linear models (Kim and Hovy, 2006)
• Boosting (Kobayashi et al., 2007)
• Mixture models (Mei et al., 2007)
Bayesian models (Titov and McDonald, 2008a;
2008b)
→ レビューから属性を発見するトピックモデル
……
31. LDA (Blei et al., 2003)
• 文書(= 単語の集合)の生成モデル
~ Dir ( )
z ~ Dir ( )
α θ z w
z ~ Multi ( )
w ~ Multi ( z )
潜在トピック β φ
単語
• 潜在トピックの発見
– 単語 w がトピック z から生成される p(w|z)
– あるトピックから生成されやすい単語 = トピック語
• レビューではトピック語 = 属性?
33. Multi-grain LDA
(Titov and McDonald, 2008a)
γ αmix • 2つの粒度のパラメータから
トピックを生成
ψ v – θgl と θloc
– 文書とウィンドウレベル
r π
• Gibbs sampling を用いてパ
αgl θgl z θloc ラメータを推定
w
β φ αloc
LDA
34. 発見されたトピック
• トピックは属性と解釈可能(ラベルは人手で付与)
MP3 player
Label Top words
sound quality sound quality headphones volume bass earphones good…
connection with PC usb pc windows port transfer computer mac software…
battery battery hours life batteries charge aaa rechargeable time…
appearance case pocket silver screen plastic clip easily small blue…
Hotel
Label Top words
amenities coffee microwave fridge tv ice room refrigerator iron…
food and drink food restaurant bar good dinner service breakfast ate eat…
staff staff friendly helpful very desk extremely help directions…
internet internet free access wireless use lobby high computer …
35. トピック-属性の対応付け
• MG-LDA の欠点
– トピックの解釈は不明
– 先ほどの表では人手で解釈,ラベル付け
• レビューのメタデータの利用
Label Top words
属性 評価値 ??? delicious soup chicken eat…
Food: 5; Decor: 5; Service 5 ??? service staff rude …
The chicken was great. On top of that ??? dark look old-fashoed…
our service was excellent and the price ??? price dollar reasonable…
was right. Can’t wait to go back!
… ….
レビューデータ トピック
36. Multi-Aspect Sentiment Model
(Titov and McDonald, 2008b)
γ αmix
ψ v
r π トピックから
評価値を生成
αgl θgl z θloc y
w
β φ αloc
LDA
37. 対応付け結果
Hotel の review
Aspect Top words
service staff friendly helpful service desk concierge excellent
location hotel walk location station metro walking away right
rooms room bathroom shower bed tv small water clean
--- breakfast free coffee internet morning access
--- $ night parking rate price paid day euros got cost
メタデータに出現する属性を対応付け
余ったトピックには対応付けなし
38. 評判情報の要約:まとめ
• 研究の主流は分類から要約にシフトしつつある
– 評判情報の抽出ともいう
• 属性(aspect)に着目する要約
– 評価者や比較対象なども(小林ら, 2006; Jindal and Liu 2008)
• 事例紹介
– Bayesian models (Titov and McDonald, 2008a,2008b)
– スタンダードな枠組みはこれから(生成モデルが流行り?)
43. 辞書構築に関する研究
• 語彙ネットワーク
– Clustering (Hatzivassiloglou and McKeown, 1997)
– Shortest-path (Kamps, 2004)
– Bootstrapping (Hu and Liu, 2004)
– Spin model (Takamura et al., 2005)
– PageRank (Esuli et al., 2007)
……
• 共起
– PMI (Turney, 2002)
– Bootstrapping (Kanayama and Nasukawa 2006)
– Lexico-syntactic pattern (Kaji and Kitsuregawa, 2006,2007;
Tokuhisa et al., 2008)
……
44. Shortest-path
• 語彙ネットワーク
– WordNet の類義関係のみ
– 肯定同士,否定同士がリンクで結ばれる
• アルゴリズム
– Seed からの shortest-path により決定
bad
good
sad
45. Bootstrapping
• 語彙ネットワーク
– WordNet の類義関係と反義関係
• アルゴリズム
– Seed と隣接するノードの肯否定を再帰的に決定
– Shortest-path + bootstrapping
swift swift swift
good good good
tardy tardy tardy
同義関係
反義関係
46. Spin Model (1/3)
• 重み付き語彙ネットワーク
– WordNet の類義関係,反義関係
反義(負) 同義(正)
1.5 great
tardy 1.5 bad
good - 1.0
2.0 2.0
1.5 sad
swift
slow
47. Spin Models (2/3)
• 確率モデル
– ノードの肯定(+1),否定(-1)を生成
– 平均場近似の適用
リンクの重み ノードの肯否定
(±1)
P( x | w) exp wij xi x j
i, j
肯否定が一致 → wij
肯否定が不一致 → -wij
48. Spin Model (3/3)
改善
Dataset1
#Seeds Spin model Shortest-path
14 73.4 70.8
4 71.0 64.9
2 68.2 66.9
改善
Dataset2
#Seeds Spin model Bootstrapping
14 83.6 72.8
4 82.3 73.2
2 83.5 71.1
(Takamura et al., 2005)
58. 参考文献1
• Andrea Esuli and Fabrizio Zebastiani, “PageRanking WordNet Synsets: An
Application to Opinion Mining”, ACL07
• Michael Gamon, Anthony Aue, Simon Corston-Oliver, and Eric Ringger,
“Pulse: Mining Customer Opinions from Free Text”, CIDA05
• Vasileios Hatzivassiloglou and Kathleen R. McKeown, “Predicting the
Semantic Orientation of Adjectives”, ACL97
• Minqing Hu nad Bing Liu, “Mining and Summarizing Customer Reviews”,
KDD04
• Nitin Jindal and Bing Liu, “Identifying Comparative Sentences in Text
Documents”, SIGIR06
• Nobuhiro Kaji and Masaru Kitsuregawa, “Automatic Construction of
Polarity-tagged Corpus from HTML Documents”, COLING/ACL06
• Nobuhiro Kaji and Masaru Kitsuregawa, “Building Lexicon for Sentiment
Analysis from Massive Collection of HTML Documents”, EMNLP07
59. 参考文献2
• Jaap Kamps, Maarten Marx, Robert J. Mokken, and Maarten de Rijke,
“Using WordNet to Measure Semantic Orientation of Adjectives”, LREC04
• Hiroshi Kanayama and Tetsuya Nasukawa, “Deeper Sentiment Analysis
Using Machine Translation Technology”, COLING04
• Hiroshi Kanayama and Tetsuya Nasukawa, “Fully Automatic Lexicon
Expansion for Domain-oriented Sentiment Analysis”, EMNLP07
• Soo-Min Kim and Eduard Hovy, “Extracting Opinions, Opinion Holders, and
Topics Expressed in Online News Media Text”, COLING/ACL06 Workshop
on Sentiment and Subjectivity in Text
• Nozomi Kobayashi, Kentaro Inui, and Yuji Matsumoto, “Extracting Aspect-
Evaluation and Aspect-of Relations in Opinion Mining”, EMNLP07
• Moshe Koppel and Jonathan Schler, “Using Neutral Examples for Learning
Polarity”, FINEXIN05
60. 参考文献3
• Taku Kudo and Yuji Matsumoto, “A Boosting Algorithm for Classification of
Semi-Structured Text”, EMNLP04
• Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, “Thumbs up?
Sentiment Classification using Machine Learning Techniques”, EMNLP02
• Bo Pang and Lillian Lee, “A Sentiment Education: Sentiment Analysis Using
Subjectivity Summarization Based on Minimum Cuts”, ACL04
• Bo Pang and Lillian Lee, “Seeing stars: Exploiting class relationships for
sentiment categorization with respect to rating scales”, ACL05
• Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar,
“Structured Models for Fine-to-Coarse Sentiment Analysis”, ACL07
• Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai,
“Topic Sentiment Mixture: Modeling Facets and Opinons in Weblogs”,
WWW07
61. 参考文献4
• Tetsuya Nasukawa and Jeonghee Yi, “Sentiment Analysis: Capturing
Favorability Using Natural Language Processing”, K-CAP03
• Jon Oberlander and Scott Nowson, “Whose Thumb Is It Anyway?
Classifying Author Personality from Weblog Text”, COLING/ACL06
• Hiroya Takamura, Takashi Inui, and Manabu Okumura, “Extracting
Semantic Orientations of Words using Spin Model”, ACL05
• Ivan Titov and Ryan McDonald, “Modeling Online Reviews with Multi-grain
Topic Models”, WWW08
• Ivan Titov and RyanMcDonald, “A Joint Model for Text and Aspect Ratings
for Sentiment Summarization”, ACL08
• Ryoko Tokuhisa, Kentaro Inui, and Yuji Matsumoto, “Emotion Classification
Using Massive Examples Extracted from the Web”, COLING08
• Peter Turney, “Thumbs Up or Thumbs Down? Semantic Orientation
Applied to Unsupervised Classification of Reviews”, ACL02
62. 参考文献5
• 池田大輔, 南野朋之, and 奥村学, “blogの著者の性別推定”, 言語処理学
会全国大会, 2006
• 岡野原大輔 and 辻井潤一, “レビューに対する評価指標の自動付与”, 自
然言語処理, Volume 14, Number 3, 2007
• 小林大祐, 松村真宏, and 石塚満, “ブログ記事の書き手の男女分類”, 言
語処理学会全国大会併設ワークショップ「感情・評価・態度と言語」, 2006
• 小林のぞみ, 乾健太郎, 松本裕二, “意見情報の抽出/構造化のタスク仕
様に関する考察”, 情報処理学会研究報告NL171-18, 2006