Extracting What
We Think and How
We Feel from What
We Say in Social
Media---- Subjective Information Extraction
Subjective...
Directions
• From coarse-grained to fine-grained
– Document level -> sentence level -> expression level
– General sentimen...
Subjective Information Extraction, Lu Chen 3
Extracting a diverse and richer
set of sentiment-bearing
expressions, includi...
Approach
Subjective Information Extraction, Lu Chen 4
Extracting
Candidate Expressions
Identifying
Inter-Expression Relati...
Extracting Candidate Expressions
• Root word: a word that is considered sentiment-bearing in general
sense.
• Collecting r...
Identifying Inter-Expression Relations
• Connecting the candidate expressions via two types of inter-
expression relations...
An Example
1. I saw The Avengers yesterday evening. It was long but it was very good!
2. I do enjoy The Avengers, but it's...
Assessing Target-dependent Polarity
• For each candidate expression ,
– P-Probability – the probability that indicates pos...
An Optimization Model
• We want the consistency and inconsistency probabilities derived
from the the P-Probabilities and N...
The Example
Subjective Information Extraction, Lu Chen 10
Evaluation
• Datasets:
– 168,005 tweets about movies
– 258,655 tweets about persons
• Gold standard:
– 1,500 tweets labele...
Subjective Information Extraction, Lu Chen 12
Subjective Information Extraction, Lu Chen 13
Application
Subjective Information Extraction, Lu Chen 14
Subjective Information Extraction, Lu Chen 15
Relevance of User Groups Based on Demographics and
Participation to Social M...
User Categorization
Subjective Information Extraction, Lu Chen 16
Engagement Degree
Tweet Mode
Content Type
Political Pref...
Electoral Prediction with Different User Groups
Subjective Information Extraction, Lu Chen 17
Revealing the challenge of i...
Electoral Prediction with Different User Groups
Subjective Information Extraction, Lu Chen 18
Prediction of user’s vote ba...
Emotion
• Discovering Fine-grained Sentiment in Suicide Notes: Classify each
sentence from suicide notes into 15 emotional...
What’s next?
Subjective Information Extraction, Lu Chen 20
static
dynamic
coarse-grained fine-grained
subjective informati...
Thank you !
Subjective Information Extraction, Lu Chen 21
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Extracting What We Think and How We Feel from What We Say in Social Media

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  • Research goalGenerally speaking, the information of what we think and how we feel is subjective information, so such information extraction task can be called subjective information extractionA large part of information we post in social media is about what we think and how we feel. For example, #nervous, movie The Avengers, it’s important for sentiment analysis to provide more actionable insight.
  • coordinate frameImagine the original point is the “traditional sentiment analysis”, the basic task of which is to classify the polarity of a given text.Basically, I am working towards two directions, one aims to get more fine-grained subjective information, the other one aims to capture the dynamics of subjective information.“Fine-grained” information can be pursued from different angle. For example, in sentiment analysis, identifying the polarity of sentiment expressions in a document is more fine-grained than classifying the overall polarity of a document. It is well-known that sentiment is sensitive to the domain or even the target. Identifying the sentiment associated with a specific target (e.g., a specific person or product feature) is more fine-grained than assessing the general sentiment. Another angle to pursue the fine-grained subjectivity is to consider sentiment as one type of subjective information. For example, emotion, as another type of subjective information, can be more fine-grained. In addition, we can also discriminate other types of subjective information from sentiment. In social media, people often post something like “On the way to watch a movie, hope it’s good”, or “A friend recommended me a new restaurant, I want to try it.” Currently, they are likely to be classified as positive sentiment. But the first one is actually expectation and the second one is intent.Another direction is from static to dynamic. Have your attitude about something or some person even been changed during social communication? I guess we all have. It is appealing to have tools to detect and track such changes, and discover what leads to such changes. One specific research topic along this line is to study the persuasion campaign in social media.
  • Extracting What We Think and How We Feel from What We Say in Social Media

    1. 1. Extracting What We Think and How We Feel from What We Say in Social Media---- Subjective Information Extraction Subjective Information Extraction, Lu Chen 1 Lu Chen Kno.e.sis Center Wright State University http://cdryan.com/blog/think-feel/
    2. 2. Directions • From coarse-grained to fine-grained – Document level -> sentence level -> expression level – General sentiment -> domain-dependent sentiment -> target-dependent sentiment – Sentiment  Subjective information • Sentiment (positive/negative/neutral) -> emotion (happy, sad, angry, surprise, etc.) • Other types of subjective information: Intent, suggestion/recommendation, wish/expectation, outlook, viewpoint, etc. • From static to dynamic – Our attitude can be changed during social communication. • Modeling, detecting, and tracking the change of attitude • What leads to the change of attitude? E.g., persuasion campaign Subjective Information Extraction, Lu Chen 2 static dynamic coarse-grained fine-grained subjective information
    3. 3. Subjective Information Extraction, Lu Chen 3 Extracting a diverse and richer set of sentiment-bearing expressions, including formal and slang words/phrases Assessing the target-dependent polarity of each sentiment expression A novel formulation of assigning polarity to a sentiment expression as a constrained optimization problem over the tweet corpus Extracting Diverse Sentiment Expressions With Target-dependent Polarity from Twitter Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, and Amit P. Sheth
    4. 4. Approach Subjective Information Extraction, Lu Chen 4 Extracting Candidate Expressions Identifying Inter-Expression Relations Assessing Target-dependent Polarity
    5. 5. Extracting Candidate Expressions • Root word: a word that is considered sentiment-bearing in general sense. • Collecting root words from – General-purpose sentiment lexicons: MPQA, General Inquirer, and SentiWordNet – Slang dictionary: Urban Dictionary • For each tweet, selecting the “on-target” root words, and extracting all the n-grams that contain at least one selected root word as candidates Subjective Information Extraction, Lu Chen 5
    6. 6. Identifying Inter-Expression Relations • Connecting the candidate expressions via two types of inter- expression relations – consistency relation and inconsistency relation • Basic ideas: – A sentiment expression is inconsistent with its negation; two sentiment expressions linked by contrasting conjunctions are likely to be inconsistent. – Two adjacent expressions are consistent if they do not overlap, and there is no extra negation applied to them or no contrasting conjunction connecting them. Subjective Information Extraction, Lu Chen 6
    7. 7. An Example 1. I saw The Avengers yesterday evening. It was long but it was very good! 2. I do enjoy The Avengers, but it's both overrated and problematic. 3. Saw the avengers last night. Mad overrated. Cheesy lines and horrible writing. Very predictable. 4. The avengers was good but the plot was just simple minded and predictable. 5. The Avengers was good. I was not disappointed. Subjective Information Extraction, Lu Chen 7
    8. 8. Assessing Target-dependent Polarity • For each candidate expression , – P-Probability – the probability that indicates positive sentiment – N-Probability – the probability that indicates negative sentiment • For each pair of candidate expressions and , – Consistency probability – the probability that and have the same polarity: – Inconsistency probability – the probability that and have different polarities: Subjective Information Extraction, Lu Chen 8 ic )(Pr i P c )(Pr i N c ic ic 1)(Pr)(Pr  i N i P cc ic jc ic jc )(Pr)(Pr)(Pr)(Pr),(Pr j N i N j P i P ji cons cccccc  ic jc )(Pr)(Pr)(Pr)(Pr),(Pr j P i N j N i P ji incons cccccc 
    9. 9. An Optimization Model • We want the consistency and inconsistency probabilities derived from the the P-Probabilities and N-Probabilities of the candidates will be closest to their expectations suggested by the relation networks. • Objective Function: Subjective Information Extraction, Lu Chen 9                1 1 22 ),(Pr1),(Pr1minimize n i n ij ji inconsincons ijji conscons ij ccwccw where and are the weights of the edges (the frequency of the relations) between and in the consistency and inconsistency relation networks, and n is the total number of candidate expressions. ic jc cons ijw incons ijw
    10. 10. The Example Subjective Information Extraction, Lu Chen 10
    11. 11. Evaluation • Datasets: – 168,005 tweets about movies – 258,655 tweets about persons • Gold standard: – 1,500 tweets labeled with sentiment expressions and overall polarities for the movie targets – 1,500 tweets labeled with sentiment expressions and overall polarities for the person targets • Baseline methods: – MPQA, GI, SWN: For each extracted root word regarding the target, simply look up its polarity in MPQA, General Inquirer and SentiWordNet, respectively. – PROP: a propagation approach proposed by Qiu et al. (2009) – COM-const: Assign 0.5 to all the candidates as their initial P-Probabilities. – COM-gelex: Initialize the candidates’ polarities according to the root word set. Subjective Information Extraction, Lu Chen 11 Reference: Qiu, G.; Liu, B.; Bu, J.; and Chen, C. 2009. Expanding domain sentiment lexicon through double propagation. In Proc. of IJCAI.
    12. 12. Subjective Information Extraction, Lu Chen 12
    13. 13. Subjective Information Extraction, Lu Chen 13
    14. 14. Application Subjective Information Extraction, Lu Chen 14
    15. 15. Subjective Information Extraction, Lu Chen 15 Relevance of User Groups Based on Demographics and Participation to Social Media Based Prediction -- -- A Case Study of 2012 U.S. Republican Presidential Primaries Lu Chen, Wenbo Wang, and Amit P. Sheth • Existing studies on predicting election result are under the assumption that all the users should be treated equally. • How could different groups of users be different in predicting election results? 1. Providing a detailed analysis of the social media users on different dimensions 2. Estimating the “vote” of each user by analyzing his/her tweets, and predicted the results based on “vote-counting” 3. Examining the predictive power of different user groups in predicting the results of Super Tuesday races in 10 states
    16. 16. User Categorization Subjective Information Extraction, Lu Chen 16 Engagement Degree Tweet Mode Content Type Political Preference Location
    17. 17. Electoral Prediction with Different User Groups Subjective Information Extraction, Lu Chen 17 Revealing the challenge of identifying the vote intent of “silent majority” Retweets may not necessarily reflect users' attitude.
    18. 18. Electoral Prediction with Different User Groups Subjective Information Extraction, Lu Chen 18 Prediction of user’s vote based on more opinion tweets is not necessarily more accurate than the prediction using more information tweets The right-leaning user group provides the most accurate prediction result. It correctly predict the winners in 8 out of 10 states. To some extent, it demonstrates the importance of identifying likely voters in electoral prediction.
    19. 19. Emotion • Discovering Fine-grained Sentiment in Suicide Notes: Classify each sentence from suicide notes into 15 emotional categories, e.g., love, pride, guilt, blame, hopelessness, etc. • Emotion Identification from Twitter Data: 7 emotion categories, including joy, sadness, anger, lover, fear, thankfulness, and surprise – Can we automatically create a large emotion dataset with high quality labels from Twitter? How? – What features can effectively improve the performance of supervised machine learning algorithms? – How much performance will be gained by increasing the size of the training data? – Can the system developed on Twitter data be directly applied to identify emotions from other datasets? Subjective Information Extraction, Lu Chen 19
    20. 20. What’s next? Subjective Information Extraction, Lu Chen 20 static dynamic coarse-grained fine-grained subjective information Detecting the change of attitude during persuasive communication Discriminating other types of subjective information from sentiment, e.g., wish, intent
    21. 21. Thank you ! Subjective Information Extraction, Lu Chen 21

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