Approaches for Automatically Tagging Affect Nathanael Chambers, Joel Tetreault, James Allen University of Rochester Department of Computer Science

Affective Computing Why use computers to detect affect? –Make human-computer interaction more natural Computers express emotion And detect user’s emotion Tailor responses to situation –Use affect for text summarization Understanding affect improves computer- human interaction systems

From the Psychologist’s P.O.V However, if computers can detect affect, it can also help humans understand affect By observing the changes in emotion and attitude in people conversing, psychologists can determine correct treatments for patients

Marriage Counseling Emotion and communication are important to mental and physical health Psychological theories suggest that how well a couple copes with serious illness is related to how well they interact to deal with it Poor interactions (ie. Disengagement during conversations) can at times exacerbate an illness Tested hypothesis by observing the engagement- levels of conversation between married-couples presented with a task

Example Interactions Good interaction sequence: W: Well I guess we'd just have to develop a plan wouldn't we? H: And we would be just more watchful or plan or maybe not, or be together more when the other one went to do something W: In other words going together H: Going together more W: That's right. And working more closely together and like you say, doing things more closely together. And I think we certainly would want to share with the family openly what we felt was going on so we could kind of work out family plans Poor interaction sequence: W: So how would you deal with that? H: I don't know. I'd probably try to help. And you know, go with you or do things like that if I, if I could. And you know, I don't know. I would try to do the best I could to help you

Testing theory Record and transcribe conversations of married couples presented with “what-if” scenario of one of them having Alzheimer’s. –Participants asked to discuss how they would deal with the sickness Tag sentences of transcripts with affect-related codes. Certain textual patterns evoke negative or position connotations Use distribution of tags to look for correlations between communication and marital satisfaction Use tag distribution to decide on treatment for couple

Problem However tagging (step 2) is time- consuming and requires training time for new annotators, as well as being unreliable Solution: use computers to do tagging work so psychologists can spend more time with patients and less time coding

Goals Develop algorithms to automatically tag transcripts of a Marriage Counseling Corpus (Shields, 1997) Develop a tool that human annotators can use to pre-tag a transcript given the best algorithm, and then quickly correct it

Outline Background Marriage Counseling Corpus N-gram based approaches Information-Retrieval/Call Routing approaches Results CATS Tool

Background Affective computing, or detecting emotion in texts or from a user, is a young field Earliest approaches used keyword matching Tagged dictionaries with grammatical features (Boucouvalas and Ze, 2002) Statistical methods – LSA (Webmind project), TSB (Wu et al., 2000) to tag a dialogue Liu et al. (2003) use common-sense rules to detect emotion in s

New Methods for Tagging Affect Our approaches differ from others in two ways: Use different statistical methods based on computing N-grams Tag individual sentences as opposed to discourse chunks Our approaches are based on methods that have been successful in another domain: discourse act tagging

Marriage Counseling Corpus 45 annotated transcripts of married couples working on a task of Alzheimer’s Collected by psychologists in the Center for Future Health, Rochester, NY Transcripts broken into “thought units” – one or more sentences that represent how the speaker feels toward a topic (4,040 total) Tagging thought units takes into account positive and negative words, level of detail, comments on health, family, travel, etc, sensitivity

Code Tags DTL – “Detail” (11.2%) speaker’s verbal content is concise and distinct with regards to illness, emotions, dealing with death: –“It would be hard for me to see you so helpless” GEN – “General” (41.6%) verbal content towards illness is vague or generic, or speaker does not take ownership of emotions: –“I think that it would be important”

Code Tags SAT: “Statements About the Task” – (7.2%) couple discusses what the task is, how to perform it: –“I thought I would be the caregiver” TNG – “Tangent” – (2.9%) statements that are way off topic. ACK – “Acknowledgments” (22.8%) of the other speaker’s comments: –“Yeah” “right”

N-Gram Based Approaches n-gram: a sequential list of n words, used to encode the likelihood that the phrase will appear in the future Involves splitting sentence into chunks of consecutive words of length “n” “I don’t know what to say” 1-gram (unigram): I, don’t, know, what, to, say 2-gram (bigram): I don’t, don’t know, know what, what to, to say 3-gram (trigram): I don’t know, don’t know what, know what to, etc. … n-gram

Frequency Table (Training) GEN DTL ACK “I don’t want to be” “Don’t want to be” “I” SAT “Yeah” Each entry: Probability that n-gram is labeled a certain tag

N-Gram Motivation Advantages Encode not just keywords, but also word ordering, automatically Models are not biased by hand coded lists of words, but are completely dependent on real data Learning features of each affect type is relatively fast and easy Disadvantages Long range dependencies are not captured Dependent on having a corpus of data to train from –Sparse data for low frequency affect tags adversely affects the quality of the n-gram model

Naïve Approach P(tag i | utt) = max j,k P(tag i | ngram jk ) Where i is one of {GEN, DTL, ACK, SAT, TNG} And ngram jk is the j-th ngram of length k So for all n-grams in a thought unit, find the one with the highest probability for a given tag, and select that tag

Naïve Approach Example I don’t want to be chained to a wall. kTagTop N-gramProbability 1GENdon’t GENto a GEN I don’t DTLdon’t want to be DTLI don’t want to be1.00

N-Gram Approaches Weighted Approach –Weight the longer n-grams higher in the stochastic model Lengths Approach –Include a length-of-utterances factor, capturing the differences in utterance length between affect tags Weights with Lengths Approach –Combine Weighted with Lengths Repetition Approach –Combine all the above information,with overlap of words between thought units

Repetition Approach Many acknowledgement ACK utterances were being mistagged as GEN by the previous approaches. Most of the errors came from grounding that involved word repetition: A - so then you check that your tire is not flat. B - check the tire We created a model that takes into account word repetition in adjacent utterances in a dialogue. We also include a length probability to capture the Lengths Approach. Only unigrams are used to avoid sparseness in the training data.

IR-based approaches Work based on call-routing algorithm of Chu-Carroll and Carpenter (1999) Problem: route a user’s call to a financial call center to the correct destination Do this by comparing a query from the user (speech converted to text) into a vector to be compared with a list of possible destination vectors in a database

Database Table (Training) GEN DTL ACK “I don’t want to be” “Don’t want to be” “I” SAT “yeah” Query Cosine comparison “yeah, that’s right” Database Query (thought unit) compared against each tag vector in database

Database Creation Construct database in the same manner as N-gram Database then normalized Filter: Inverse Document Frequency (IDF) – lowers the weight of terms that occur in many documents: IDF(t) = log 2 (N / d(t) ) Where d(t) is the number of tags containing n-gram t, and N is the total number of tags

Method 1: Routing-based method Modified call-routing method with entropy (amount of disorder) to further reduce contribution of terms that occur frequently Also created two more terms (rows in database) –Sentence length: tags may be correlated with sentences of a certain length –Repetition – acknowledgments tend to repeat the words stated in the previous thought unit

Method 1: Example ACK=0.002 query DTL = GEN = SAT = TNG = Cosine scores for tags compared against query vector for “I don’t want to be chained to a wall”

Method 2: Direct Comparison Instead of comparing queries to a normalized database of exemplar documents, compare them to all test sentences Advantage: no normalizing or construction of documents Cosine test is used to get the top ten matches. Add matches with the same tag. The tag that has the highest sum in the end is selected.

Method 2: Example Cosine ScoreTagSentence 0.64SATAre we supposed to get them? 0.60GENThat sounds good 0.60TNGThat’s due to my throat 0.56DTLBut if I said to you I don’t want… 0.55DTLIf it were me, I’d want to be a guinea pig to try things DTL selected with total score of 1.11

Evaluation Performed six-fold cross-validation over the Marriage Corpus and Switchboard Corpus Averaged scores from each of the six evaluations

Results NaiveWeightedLengths Weights with Lengths Repetition 66.80%67.43%64.35%66.02%66.60% 6-Fold Cross Validation for N-gram Methods OriginalEntropyRepetitionLengthRepetition and Length Direct 61.37%66.16%66.39%66.76% 63.16% 6-Fold Cross Validation for IR Methods

Discussion N-gram approaches do slightly better than IR over Marriage Counseling Incorporating additional features of sentence length and repetition improve both models Entropy model better than IDF in call-routing system (gets 4% boost) Psychologists currently using tool to tag their work. Note sometimes computer tags better than the human annotators

CATS CATS: An Automated Tagging System for affect and other similar information retrieval tasks. Written in Java for cross-platform interoperability. Implements the Naïve approach with unigrams and bigrams only. Builds the stochastic models automatically off of a tagged corpus, input by the user into the GUI display. Automatically tags new data using the user’s models. Each tag also receives a confidence score, allowing the user to hand check the dialogue quickly and with greater confidence.

The CATS GUI provides a clear workspace for text and tags. Tagging new data and training old data is done with a mouse click.

Customizable models are available. Create your own list of tags, provide a training corpus, and build a new model.

Tags are marked with confidence scores based on the probabilistic models.