13 naacl-a latent variable model-qiu and jiang-slides
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Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot is-sues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discov-ery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: view-point specific topic preference, user id and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering posts based on viewpoints and clustering users with different viewpoints.
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13 naacl-a latent variable model-qiu and jiang-slides
- 1. A Latent Variable Model for Viewpoint Discovery from Threaded Forum Posts Minghui Qiu and Jing Jiang School of Information System Singapore Management University 1
- 2. Threaded Forums • Threaded structure • With „reply-to‟ relations (User interactions) • Multiple threads on the same issue 2
- 3. Contrastive viewpoints in Threaded Forums Each Coin Has Two Sides the Chinese athlete Liu Xiang quit the London Olympic game Pro Obama or Anti Obama? How to find contrastive viewpoints from threaded forum posts? 3
- 4. Task and Method Overview Finding viewpoints for posts Finding viewpoints for users A set of corpus on one controversial issue Method • A unified model for finding contrastive viewpoints (two-viewpoint) from threaded forum posts • We build our model based on three observations 4
- 5. Observation 1: Different Viewpoints Will Have Different Topic Preference • Our findings on ``LiuXiang” data set (``Will you support LiuXiang after he failed in London Olympic game?‟‟) 0.16 0.14 0.12 disappointed, athlete, ad sponsors Support LiuXiang Against LiuXiang Olympic hero, sympath y on his injury 0.1 0.08 0.06 0.04 0.02 0 21 34 39 28 22 6 19 31 4 37 14 8 16 12 13 30 17 11 7 18 Topic focus of two viewpoints on “LiuXiang” Data Set 5
- 6. Observation 1: Different Viewpoints Will Have Different Topic Preference • Framing1 – Users with different sentiments/positions would focus on different aspects of the topic. E.g.: – For “iPhone” users: “hardware and build”, “siri”, “ios” – Against “iPhone” users: “physical keyboard”, “android”, “galaxy” • Model assumption – Each viewpoint has its own topic distribution 1D. Tversky, Amos; Kahneman. The framing of decisions and the psychology of choice. pages 453–458, 1981. 6
- 7. Observation 2: the Same User Will Hold the Same Viewpoint Towards an Issue • User consistency – Posts from the same user tend to have the same viewpoint towards an issue – A viewpoint can be derived from the set of posts towards the same issue grouped by the same user ID • Model assumption – There is a user-level viewpoint distribution – For each post by a user, its viewpoint is drawn from the corresponding user‟s viewpoint distribution 7
- 8. Observation 3: User Interactions Reveal User Viewpoints • User interaction – User interaction: a post in reply to another user – Users with the same viewpoint tend to have positive interactions among themselves, while with different viewpoint tend to have negative interactions • Sample positive and negative interactions 8
- 9. Observation 3: User Interactions Reveal User Viewpoints • Model assumption – Interaction polarity is generated based on the viewpoint of the current post and the viewpoint of recipient post(s) User 1 Id 2 Viewpoint v1 User 2 Content Post Id V1 2 V1 V1 5 ? … Positive Interaction 1 3 I agree with your post Dan. Obama is so … Viewpoint ? p(POS): p(NEG): 1 - p(POS) Y 9
- 10. Overview of the Model • A probabilistic model based on three observations – Each viewpoint‟s topic preference – User consistency – User interaction 10
- 11. Related Works • Topic-Aspect Model (TAM, Paul et al., AAAI‟10) – A viewpoint-topic model where viewpoint and topic are orthogonal – No user interaction • Cross-Perspective Topic Model (Fang et al., WSDM‟12) – Supervised model • Subgroup detection – Mining user opinions (Abu-Jbara et al., ACL‟12) – User interaction (Hassan et al., EMNLP‟12) – Does not model viewpoints 11
- 12. A Probabilistic Model Topic specific word distribution Viewpoint specific topic distribution Y T w •U: # of users •N: # of posts •L: # of words •z: a topic label •x: a switch •x=0: w is background word •x=1: w is topical word •y: a viewpoint label •s: a interaction type z x User-level viewpoint distribution L y s Interaction type N U The polarity of interaction type is learnt beforehand. 12
- 13. Polarity Prediction for Interaction Type • Supervised learning – Requiring labeled data • Unsupervised approach – Sample sentence: I agree with you – Finding interaction expressions • Finding sentences contains mentions of the recipient (user name or 2nd-person pronoun). E.g. you • Surrounding words: a text window of 8 words. E.g.: I agree – Interaction polarity • Positive if there are more positive sentiment words, otherwise negative 13
- 14. Evaluation • Data Sets – English Data Sets • Three most discussed threads from Abu-Jbara et al., ACL‟12 – Chinese Data Sets • Three popular controversial issues in TianYaClub (one of the most popular Chinese online forums) • Statistics 14
- 15. Data Annotation • Identification of viewpoints – 150 randomly sampled posts, two annotators (Cohen‟s kappa agreement ≥ 0.61) • Identification of user groups – 150 randomly sampled users, two annotators (Cohen‟s kappa agreement ≥ 0.70) To label a user‟s viewpoint is easier than to label a post‟s viewpoint 15
- 16. Baselines • Topic-Aspect Model (TAM, Paul et al., AAAI‟10) – A viewpoint-topic model where viewpoint and topic are orthogonal • Degenerate variants of our model – UIM: User interaction model (part of our model) – JVTM: Joint viewpoint-topic model (our model without interaction) – JVTM-G: JVTM with a global viewpoint distribution 16
- 17. Identification of Viewpoints • Task – To identify each post‟s viewpoint • Results • Our model significantly outperforms other models (at 10% significance level) • Effectiveness of assumptions • • • Each viewpoint’s topic preference: JVTM > TAM User consistency: JVTM > JVTM-G User interaction: JVTM-UI > others • User interaction is more important than other factors Averaged results of the models in identification of viewpoints 17
- 18. Identification of User Groups • Subgroup detection – To detect ideological subgroups, i.e.: user groups with different viewpoints • Results • Our model significantly outperforms other methods (at 10% significance level) • Effectiveness of assumptions • • • Each viewpoint’s topic preference: JVTM > TAM User consistency: JVTM > JVTM-G User interaction: JVTM-UI > others Averaged results of the models in identification of viewpoints 18
- 19. Qualitative Analysis • User interaction network on “will you vote obama” Green (left) and white (right) nodes represent users with two different viewpoints discovered by our model. Red (thin) edges represent negative interactions while blue (thick) edges represent positive interactions More intra-cluster positive interactions and More inter-cluster negative interactions 19
- 20. Qualitative Analysis • Users with different viewpoints tend to have different topic focus 0.16 Support LiuXiang 0.14 Against LiuXiang 0.12 0.1 0.08 0.06 0.04 0.02 0 21 34 39 28 22 6 19 31 4 37 14 8 16 12 13 30 17 11 7 18 Topic focus of two viewpoints on “LiuXiang” Data Set 20
- 21. Qualitative Analysis • Top 4 topics for “supporting LiuXiang” viewpoint Word Translation Word Translation Word 刘翔 LiuXiang 栏 hurdle 运动员 athlete 第一 first 冠军 champion 伤 injury 奥运会 时间 time 赛后 after-game 成绩 record 跟腱 Olympic Achilles's tendon 奥运 Olympic 田径 摔倒 fall 北京 beijing 获得 achieve 男子 track and field man 13秒 13s 脚 foot 一个 one 最后 finally 手术 surgery london 届 time 刘 liu 决赛 final 伦敦 田联 IAAF 情况 condition 奥运会 Olympic 英国 Britain 医生 doctor train 参加 attend 受伤 hurt 上海 Shang Hai 训练 重 跑 run field 导致 result in already broken 记者 好 reporter 已经 赛场 断裂 good 遗憾 纪录 record 英雄 hero 团队 team 联赛 12秒 12s first heat 需要 that time retire 夺冠 跳 跑道 champion 当时 退役 预赛 2012年 罗伯斯 pity league matches need jump report 第二 伟大 2nd great 2012 Robles Translation Word Translation heavy 21
- 22. Qualitative Analysis • Top 4 topics for “against LiuXiang” viewpoint Word 帖 社区 Translation post community Word 发自 随时 Translation orgin from anytime Word 天涯 楼主 Translation tianya poster Word 天涯 抵制 Translation tianya Resist 热点 hot 老板 boss 猫 sneak 骗子 lier 围观 apathetic 政协 CPPCC 妈 F**K 体坛 sports 傻逼 fool 帮 those 水 spam 最 钱 水军 笑 骂 孙子 least money spam laugh scold foolish medal 唯金牌论 gold theory only smile 微笑 support 顶 nausea 恶心 可口可乐 Coca Cola drink 喝 joke 笑话 孙子 啤酒 杨 全家 别有用心 躲 foolish bear yang whole family ulterior motive hide 提 吃 牌 苦笑 高尚 有力 你们 you 加油 cheer up 歪风 bad tendency 劳民伤财 多么 extremly 脱离 有人 someone 枪眼 脸上 face 神位 separate 看看 force of public 滩 opinion fame 精神 look 黑 mention eat medal bitter smile noble powerful a waste of money and manpower spam those 黄继光 a hero spirit 神像 fame 22
- 23. Summary • Conclusion • A viewpoint discovery model for threaded forums • Modeling three observations • Viewpoint-specific topic distribution (Framing) – User consistency – Interplay between user interactions and viewpoints – Future work – – – – Document representation: complex lexical units A more accurate interaction polarity classifier Contrastive viewpoint summarization Mining controversial issues and finding viewpoints 23
- 24. Thank you 24
- 25. Reference • [Paul et al., AAAI‟10] Paul, M. J. and Girju, R. (2010). A twodimensional topic-aspect model for discovering multi-faceted topics. In AAAI. • [Abu-Jbara et al., ACL‟12] Amjad Abu-Jbara et al. (2012), Subgroup detection in ideological discussions. In ACL. • [Yi Fang et al. WSDM‟12] Yi Fang et al. (2012), Mining contrastive opinions on political texts using cross-perspective topic model. In WSDM, pages 63–72. • [Abu-Jbara et al., ACL‟12] Amjad Abu-Jbara et al., (2012). Subgroup detection in ideological discussions. In ACL. • [Hassan et al., EMNLP‟12] Hassan et al., (2012). Detecting subgroups in online discussions by modeling positive and negative relations among participants. In EMNLP. 25
Editor's Notes
- Users with the same viewpoint tend to have positive interactions among themselvesUsers with different viewpoints tend to have negative interactions among themselves
- The polarity of an interaction expression is generated based on the viewpoint of the current post and the viewpoint of the post(s) that the current post replies to