Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers
Authors: Moeen Mostafavi
Michael D. Porter
Dawn T Robinson
Venue: ICLR 2022 Submitted (2022)
Keywords: Affect Control Theory
Bidirectional Encoder Representations from Transformers
affective lexicon
formal theory

Abstract: Cultural sentiments of a society characterize social behaviors, but modeling sentiments to manifest every potential interaction remains an immense challenge. Affect Control Theory (ACT) offers a solution to this problem. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexicon. Traditionally, the sentiments are quantified using survey data which is fed into a regression model to explain social behavior.  The lexicons used in the survey are limited due to prohibitive cost.  This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for developing a replacement for these surveys. This model achieves state-of-the-art accuracy in estimating affective meanings, expanding the affective lexicon, and allowing more behaviors to be explained. 

Under review as a conference paper at ICLR 2022
LEARNING AFFECTIVE MEANINGS THAT DERIVES THE SOCIAL BEHAVIOR USING BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS
Anonymous authors Paper under double-blind review
ABSTRACT
Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is generative theory of culture and behavior based on three-dimensional sentiment lexicon. Traditionally, the sentiments are quantified using survey data which is fed into a regression model to explain social behavior. The lexicons used in the survey limited due prohibitive cost. This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to develop replacement for these surveys. This model achieves state-of- the-art accuracy in estimating affective meanings, expanding the affective lexicon, and allowing more behaviors be explained.
INTRODUCTION
Consider talking to your mentor for some advice about how behave with your colleague. Your mentor probably starts asking questions about the culture in the workspace and may continue asking about the identity of the person. These questions could be about institutional constraints such as being the manager, or they could be personality sentiment such as being nice or active. Knowing this information, your mentor may have some initial recommendations, but you adapt your behavior after observing the reactions from the colleague. This is descriptive scenario for daily interaction. Affect Control Theory (ACT) quantifies the variables in this scenario and produces equations for simulating human behavior in social interactions.
ACT was introduced in the 1970s (Heise) 1977) and has been validated in more than 100 social science projects (Robinson & Smith-Lovin 2018). ACT has been used in interdisciplinary appli- cations such as Human Human-Computer Interactions (Robillard & Hoey 2018), finding how language cultures affect social response (Kriegel al., 2017), and modeling identities and behaviors within groups (Rogers & Smith-Lovin 2019). More recently, Mostafavi (202T) introduced ACT to esti- mate and track emotional states during online messaging. For example, chatbots can the ACT framework to understand the emotional state of the customer in real time and adapt their behavior accordingly. While the potential uses of ACT are understanding emotional changes during online messaging Mostalavi (202I) real life applications are limited due to the vocabulary size of affective dictionaries.
ACT uses a three-dimensionss affective meaning space as a quantified form of sentiments (Heise 1977]. ACT uses Evaluation [good vs. bad], Potency [powerful vs. powerless], and Activity [active vs. passive] (EPA) space introduced by [Osgood et al.] (1957) as semantic differential form of affective meaning. These affective lexicons represent the word of interest within cultural and social boundaries (Robinson & Smith-Lovin 1992). Fontaine et al. (2007) found that EPA scores are the first three principal components after reducing dimensionality on 144 features representing the main components of emotions.
Historically, surveys are used to quantify the affective meanings within a cultural group. To com- pensate for unreliability in the survey, most EPA surveys designed that each word is scored by at least 25 different participants. Thus, finding the affective meaning for 5000 words requires over 125,000 ratings and 400 hours of respondent time (Heise) 2010). Because ACT also requires

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4
Behavior
Identity
Modifier
4
3
3
boss
happy
Potency
yell at
Strct
wonser vative
0
child molester
-1
nervous
suicidal
-2
beg
baby
4
-2
2
Evaluation
Figure I: Sentiments of sample words in EPA space. Circles, squares, and triangles show identities, behaviors, and modifiers. The color represents the activity dimension. Comparing words such as "happy" and "suicidal" we can observe one has positive values in all three dimensions and the other has negative values.
EPA estimates for social interactions, EPA surveys must also include participants to score a large number of additional scenarios. Due to the high cost and time required, most EPA surveys have been focused on a small number of words which has limited the applicability ACT. As an alternative conducting new surveys, researchers have tried supervised (Mostafavi & Porter 2021 2017) and semi-supervised (Alhothali & Hoey 2017) methods on shallow word-embedding to find affective lexicons. However, their performance on the activity and potency dimensions poor. In this paper, we discuss the main limitations for the applicability of shallow word-embeddings In this work, we use deep centence-embeddeee
Li al.,
to consider contexual aspects of concepts in social events. For that purpose, we show how to generate contextual data-set describing social events to train and test a deep neural network. We use Bidirectional Encoder Representations from Transformers (BERT) embedding tuned for finding affective lexicons. The performance of our approach achieves state-of-the-ar accuracy in estimating affective lexicons.
BACKGROUND
In this section, we start with review of ACT and how it uses sentiment of social characters to model interactions. Then we review related works estimate affective meanings from corpus and discuss their main limitations. Finally, we briefly review the BERT model and how we can use this model as deep embedding space.
2.1 AFFECT CONTROL THEORY
According to ACT, every concept representing interactions is quantified EPA space. The baseline EPA representation of words is known as sentiments. I EPA representation of some sample words. this plot, we can observe that "suicidal" and "nervous" both have bad, powerless, and passive meanings but "suicidal" is more negative in all three dimensions. On the other hand, "happy" is pleasant, powerful, and active word. Note the range of EPA ratings is from -4.3 to 4.3.
ACT considers social interactions events that include an actor that behaves toward an object. Extracting the Beto---aaioo-bjeeec (ABO) components of an event the first step in modeling interactions (Heise) 2010). Actor/Object in event has identity such "baby" or "boss". In some cases, the characteristics an actor/object is part of the identity. For example, the identity of a person is "nervous boss". In these cases, modifiers amalgamated to their identities. Figure. T visualization of sample words in EPA space. uses symbols to represent that a concept is evaluated identity, behavior, modifier.
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According to ACT theory, social events make impressions on ABO characters (Robinson & Smith- Lovin 1999). Consider an example of observing "a bossy employer argues with an employee". This observation leads people to evaluate both the actor and the object of the interaction are less pleasant than initial thoughts. They may also feel the employer is more powerful, and the employee is more powerless than their baseline sentiments after observing this event. Being more pleasant/powerful is impression of observing and event and it means higher value in evaluation/potency..
If actor/object behaves as expected, then impression of identity does not change far from baseline, but if actor/object does something unexpected, then a large change from the baseline is expected. Deflection is the euclidean distance between the baseline sentiments of ABO characters and their impressions following an event. If the impression of an ABO event is close to initial sentiments, deflection is small, and it gets bigger when the impression of the event leaves the initial sentiments. ACT discusses that minimizing deflection is the driving force in human activity. Highly deflecting events create social and physiological distress (Goldstein) 1989). For example, a grandmother fights with her grandchild, the grandmother and the grandchild feel distressed and prefer to do something. We may expect one side to take an action, like apologize, to bring the impression of their identities back where they views themselves in the society. This highly deflected event is very different from two soldiers fighting in a battle. The soldiers are supposed to fight with enemies in battle, so they may not feel social pressure change their behavior.
Heise (013) developed a software called INTERACT. It simulates sequential interactions between two identities and finds the behavior that minimizes the deflection. It can also predict attributes and emotions during the interaction. Consider the following set of events/interactions that we simulate using INTERACT,
1. Employee greets bossy employer.
2. Bossy employer asks employee.
3. Employee replies bossy employer.
4. Bossy employer argues with employee.
5. Employee listens to disobeys bossy employer.
The visualization in Figure 2 shows how the impression of actor/object's identity changed based on sequential interactions. Let's focus on evaluation dimension for the employer. Employer has a negative baseline evaluation but after observing the first two interactions, it increases. The first two interactions include positively rated behaviors. After second interaction, impression of the employer's identity is positively evaluated and so the next positively evaluated action, replies to, does not move it substantially. A positive behavior is expected from positive identity. However,in the fourth interaction, the employer evaluated to have an unpleasant identity after doing a negative behavior, argue with. For the fifth event, we have shown how the impression of different actions by the employee has significantly moved the states for both the actor and the object. The sequential interactions discussed here similar to mentorship example discussed the beginning. It shows how understanding the interaction dynamic can make us predict the consequences of our behavior.
ACT has rules to describe how impression of an events changes affective meaning of ABO char- acters. ACT uses either mathematical equations or descriptive forms to discuss these rules. The following two descriptive forms show how the identity of the actor is impressed by some events,
Actors seem nice when they behave in positive way toward others. This describes morality fact in ACT literature. Observing the evaluation dimension for the actors after he greets the object, we can find this behavior resulted in an impression of being nicer (getting larger evaluation) comparing to the state in the last step.
Active behaviors make the actors seem more active. Observing the boss's activity, he is considered more active after he argues with [active behavior] the employee.
As we have seen in the descriptive forms, events can change the impression of ABO characters. They move them toward or away from their currentor initial sentiments.
To formulate the process in mathematical space, we briefly review the quantification process us- ing surveys. The first step is quantifying the sentiments that are introduced identity, behavior,
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Behavior
idenity
Modifler
4
Employer
Employee
employer
3
2.5
listen
greet
Potency
1.0
1.5
argue
bossy
ask
reply to
Potency
disobey
listen
disobey
-1
05
-2
-2
0.0
emplyyee
disobey
-0.
Sentiments
-100
-2
-1
Evaluation
Evaluation
Evaluation
Figure 2: Simulating sequential events in an interaction between employee and employer. The initial sentiments for both characters are shown by star signs and the sentiments after each of four events shown by numbers. After the fourth event, based what the employee does, the final sentiment for the two characters would be one of the places shown by the question mark.
and modifier. For this purpose, at least 25 participants rate words of interest in EPA space (Heise 2010). In this survey, the participants rate how they feel about an identiy/behavior/mddffce such as "employer". After aggregating the sentiment surveys, every concept is assigned to its baseline affective meaning. The next step is identifying contributing facts that derive the impressions of events (Robinson & Smith-Lovin, 1999). For this purpose, the participants rate ABO characters again after observing set of events. For example, participants rate affective meaning "em- ployee". "greet", and "employer" after observing "the employee greets the employer". As dis- cussed earlier, the ratings of ABO could be different from the initial basements. ACT uses regres- sion models, known impression change equations, estimate these changes (Heise 2013). Let X= = [AA Ap. Ag, Be, Bp. Ba, Oc. Op. Oul" represent the EPA values/sentiments of an ABO triple, where {A, B, O} represent the ABO characters and fe, p, a} the EPA components. Consider fur- ther the two-way interactions X² [A. A.O., A Ba...... Ag O²) and three-way interactions X [A, 0,, A, A, A.B.O......BB.r A,B.O. The basic structure of impression change
equation is the linear model
x'=oX 8x2 xX3
(1)
where a, B, and y are coefficient vectors and X' represent the resulting impression after the event. Modifiers can incorporated prior to impression change by changing the baseline values/sentiments (e.g. bossy employer). Averett & Heise (1987) defined amalgamation equations similar to (2) to find the sentiments for an identity with a modifier.
A P OM VI,
-0.17
0.62
-0.14
-0.18
50.50 0
0
0.18
0
-0.11
0.63
0
2b
0.56
0.07
0
0.61
0.05
0.60
where, A, M, I, represent affective meaning of actor's identity, modifier, bascline identity and p, 8, d are vector of intercepts and coefficient matrices. Equation evaluation for the modifier and the identity.
22 is a weighted average of the
2.2 FINDING AFFECTIVE MEANING FROM TEXT
Alhothali & Hoey (2017) used graph-based sentiment lexicon induction methods to find affec- tive lexicons associated with words. They used similarity graphs to expand affective meanings to neighbor words in four different embedding spaces. They found that using both semantics and distributional-basete approaches gives the best semi-supervised result. Li et al. (2017) argued that word-embedding represent the words' general meaning, including denotative meaning, conno- tative meaning, social meaning, affective meaning, reflected meaning, collocative meaning, and the- matic meaning. However, ACT uses only affective meanings of the words. So word-embedding graph propagation that uses general meaning similarities reduces accuracy for finding affective meaning.
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Behavior
Identity
Modifier
4
judge
3
patient
convist
2
cripple
1
Potency
judge
X Patient
ACiViitY
+ Baby
0
baby
Cripple
convict
Judge
-1
patient
Convict
-2
cripplex
-2
baby
Evaluation
Figure 3: Visualization of words with different affective meaning in EPA space. Circles, squares, and triangles show identities, behaviors, and modifiers. The color represents the activity dimension.
Mostafavi & Porter 20221: Li et al. (0177) used supervised methods on shallow word-embeddings find affective meanings of the words. However, all these approaches are limited by using one representation for different meanings of a word and not considering the context.
Shallow word embeddings have only one representation for every word. On the other hand, the affective meaning of word in identity, modifier, or behavior categories are different. For example, "mother", "coach", and "fool" have very different affective meanings when they are behavior or identity, but these words have only one representation in shallow embedding space. Figure3] EPA values for some words that appear different categories, mapped very differently based on their category. For example, "baby" as an identity is a pleasant and active character but is unpleasant and passive as a behavior.
can observe how some words are
To get sense of how similar the affective meanings between categories, calculated the pairwise correlation of EPA values for words shared across various different categories (Table [[]. We observe that, while some categories maintain high association across categories, other categories, like identity and behavior in the activity and potency dimensions have low association implying they words used in different categories represent different affective meanings. From these tables, observe, activity and potency dimensions of common words between identity and behavior are too small assume they represent the same affective meanings. This suggest that ACT benefit from models that can represent contextual aspects and differentiate between different meanings of word.
Table 1: Correlation between the affective meaning of words in identity, behavior, and modifier
category of dictionary collected in 2014 "Smith-Lovinn al. 2016
Identity-Modifier
Modifier- Behavior
Identity-
Identity-Behavion Behavior
E
A
E
P
A
E
P
A
E
0.93
0.49
-0.58
0.98
0.92
-0.32
0.73
0.35
0.40
P
0.77
0.62
-0,39
0.85
0.80
-0.25
P
0.29
0.55
0.02
A
-0,45
0.33
0.98
A
-0.27
-0.30
0.67
A
-0.11
0.30
0.40
We can observe in Table T that affective meanings of the words in different categories are not nec- essarily highly correlated. For example, there is only 0.4 correlation between activity dimensions of words that appear both in identity and behavior categories.
2.3 BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS
In 2018, Google open-sourced BERT the state-of-the-ar model for wide range of Natural Lan- guage Processing (NLP) tasks (Devlin et al. 2018). Unlike models that use previous words predict the target word, BERT uses words both sides of the target word in all layers. BERT gives state of

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Data pre-processing
Tuning Neural Network
Fine tuning regression
Pecoo Teeecty 33 For
restuace
Actor regressor
Px
[c.d] rexlar watt oper aoo dixor dixerinine sgaine
0
Ax
B
Fly
Behasior reginesor
2003
Fjjzer
TIEEM
Poooee actert Dioeen Soreing
Ex
Mostterr regrossion
2I14
Ay
SECD
FiM-i
TiM-111
Object
rogrossorn
Figure 4: BERTNN pipeline. Sentences describing an event are generated in the first step and pre- processed to pass to a BERT model. Then the last two layers of the BERT model are concatenated to make contextual word-embedding. This embedding is passed to three-layer neural network to find affective meaning, and finally, the output of the neural network is fine-tuned using a regression model.
the art in many challenging NLP tasks (MCCormick & Ryan 2019). 9. BERT has been pre- pre-trained on Wikipedia and book corpora that includes more than 10000 books. This pre-trained model knows how to represent texts. We the base pre-trained version of BERT in this study find the embed- ding of a sentence that deseribes one social event. To use BERT as a contextual embedding we need perform the following processing, shown Figure (McCormick & Ryan 2019).
1. Input variables for BERT model are two sequences of numbers known as Token and Seg- ment IDs. By default, BERT assumes these sequences represent two sentences and two special tokens to indicate their relationship. The [CLS] token indicates the start of the first sentence, and the special token (SEP) comes at its end. We use BERT with only one sentence, but we have to use these special tokens.
2.
The BERT tokenizer, known WordPiece, tokenizes the input sentences. If the sentence tokens are in the vocabulary of the pre-trained model, they appear in the tokenized list without any modification. However, WordPiece assigns multiple tokens to word if the word is not its vocabulary. In that case, tokens are root vocabulary and suffix of the original word. For example, the words "affective" and "subtext" are not included in the vocabulary, WordPiece outputs [affect, ##ive] and [sub, ##ttet]] tokens where ## shows the two tokens came from a compound word.
3. Token IDs are indices the tokens in the BERT tokenizer vocabulary. Segment IDs asso- ciate the tokens to one of the two sentences. Since we are passing only one sentence, the segment ID set to for all the tokens sentence.
The main advantage of word-embeddinngs derived from BERT over shallow word-embeddings, is that BERT can take into account the context of word. This means that words can be automatically given different representations when the words is used behavior identiry. To take advantage of BERT word embedding, we should train it on synthetic data that represents the concepts and their category simultaneously.

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Table 2: Comparing different architectures for the neural network.
HL. denotes the number of
hidden layers in the network.
HL
Iterations
Neurons
Train Loss
Val Loss
3
400
NL + Linear
0.24
0.22
3
400
Linear
1.87
1.68
2000
Linear
1.63
1.73
2
400
NL + Linear
0.33
0.31
2
2000
NL + Linear
0.29
0,29
400
NL + Linear 0.49
0.47
1
2000
NL Linear 0.42
0.42
METHOD
We introduce new framework, BERTNN, that processes synthetic data, passes it to pre-trained BERT model, and fine-tunes the result to generate extended affective meaning dictionaries. The pipeline for our method (BERTNN) is shown in Figure 4 and discuss the details in this section.
The data used to train our model was generated from the affective dictionary described in Smith- Lovin et al. (2016). This dictionary was developed from surveys conducted between 2012-2014 and includes 929 identities, 814 behaviors, and 660 modifiers. To partition the data into training and test set, we used stratified sampling from affective dictionaries. The three categories of identities, behaviors, and modifiers in the affective dictionaries are one "strata". We randomly sample 85% of the words in each strata for the training and the remaining 15% are selected as the test set.
To generate each sample of the synthetic data, two identities, one modifier, and one behavior are randomly selected from the training set. Sentences with an Actor Behaves Modified Actor (ABMO) grammar is made (e.g., Employee greets bossy employer). In other words, each sample describes an event in ABMO grammar. We used 10000 sample events to train the regression model. After pre- processing and tokenizing ABMO sentences similar to "data pre-processing" part of the pipeline, they are sent to the pre-trained BERT model.
The mechanism of defining sentence embedding from the BERT model has significant impact on performance. We tried mechanisms such as using the values in the last layer, finding the mean values from the last two layers, and concatenating the last two layers. The best result came from deep embedding formed by concatenating the last two layers.
The next step is finding mapping from the sentence embedding to the affective space. Mostafavi & Porter (2021) used interactions between the features for better affective predictions. We used four- layer neural network to provide non-linear mapping from the BERT output the 12-dimensional ABMO values. In this neural network, the first three layers have 12 dense and 12 Tanh parts. Here, used hyperbolic tangent functions used the nonlinear functions in the network. The last layer is dense layer that produces the 12-dimensional vector. The Le loss used in the neural networks minimized the squared error between estimated affective meaning and the target values across all 12 dimensions. However, every ABMO character has a 3-dimensional representation in- dependent of other characters in ABMO grammar. In this case, considering four different regression models assigns a loss function for each character and fine-tunes its values. In short, linear regression models fine-tune the neural network output to find affective meanings of all the characters.
We implemented the neural network in Python using Trax package. We used stochastic gradient descent with a learning rate of 0.01 and batch size of 50. Table 2 shows the T.2 loss for several different architectures and iterations. The network with only lincar neurons has 12 neurons in the hidden layers. When nonlinear (NL) neurons are used, the hidden layers have 12 dense neurons and 12 hyperbolic tangent neurons. We found that the minimal L2 loss is achieved in 400 iterations with 3 hidden-layers and NL neurons.
The neural network output is 12-dimension vector that is highly correlated with the affective mean- ing variables. However, Root Mean Square Error (RMSE). Mean Absolute Error (MAE) the net- work output improved with regression layer. We implemented ordinary least squares Linear

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Table 3:
Performance of several models on (Smith-Lovin et al. 2016) data. Bold indicates the best
model. Our model, BERTNN, performed best in most categories.
MAE
RMSE
Correlation
P
P
P
Analogy stepW.
0.93
0.92
0.81
1.2
1.13
0.99
0.65
0.53
0.18
Analogy reegression
0.95
0.95
0.81
1.22
1.16
1.00
0.64
0.48
0.15
Identity
StepW Translation
0.58
0.63
0.57
0.80
0.83
0.72
0.85
0.78
0.68
BERINN
0.42
0.38
0.28
0.51
0.47
0.37
0.94
0.93
0.93
Analogy stepW.
1.17
0.71
0.68
1.44
0.90
0.83
0.73
0.45
0.51
Analogy regression
1.20
0.75
0.73
1,48
0.93
0.90
0.71
0.44
0.38
Behavior
StepW Translation
0.80
0.56
0.58
1.02
0.74
0.73
0.87
0.66
0.67
BERTNN
0.59
0.41
0.27
0.75
0.5
0.36
0.94
0.86
Analogy stepW.
0.33
0.73
0.88
1.08
0.9
1.09
0.87
0.86
0.57
Analogy regression
0.56
0.76
0.92
1.16
0.93
1.10
0.86
0.86
0.57
Modifier
StepW Translation
0.32
0.53
0.57
0.86
0.67
0.70
0.92
0.91
0.85
BERTNN
0.50
0.39
0.38
0.60
0.49
0.44
0.96
0.95
0.94
Regression using the Sklearn package in Python. We can share the code upon request. Also, the data used in this project is publicly available on the internet.
The tuning layers in our framework are trained based on the target affective dictionary. After train- ing, this pre-trained model can predict affective meaning for new concepts. The user should make an ABMO event that includes the concept of interest and pass to the model, and the affective meaning would be the output of the network. For example, if "moderator" is a new identity outside the affective dictionary, we make an event that includes this identity as actor or object such as, "moderator help angry client". The event is passed to a pre-trained model of this project, then the output of actor regressor is the predictive affective meaning for "moderator" identity. Here the affective meaning that we get for specific identity actor object is the same, it does not change anything we consider the identity of interest as actor or object.
4 RESULTS
We compared the performance of our model with different word analogy (Kozlowski et al. 2019) regressions (Li et al., (017). and translation matrix methods (Mostatavi & Porter [222]) to find affective meanings (T We used RMSE, MAE, and correlation analysis to compare the result. The test data used to compare these methods included 1339 identities, 122 behaviors, and 99 modifiers came from stratified sampling discussed earlier.
All the similar works used shallow embedding (Kozlowski et al. 2019 Mostafavi & Porter 2021 Li et al. 2017). We used the publicly available code of Mostatavi & Porter (2021) and Kozlowsk et al. (2019) to replicate their work and compare the result. For et al. (2017) we had to implement
their method in Python. To further improve their methods, we added tuning layers such as adding step-wise regression to the analogy method. Table 3 shows the best result we could get from other methods. We can observe from this table that our approach reached state of the art across most of the metrics.
To evaluate how close our extended dictionaries are to the baseline affective meanings. we calculated the correlation for identity and behaviors in Tables 5 and 6 The correlation (a) between the result of the test set in our method, (EF, EP, and EAA and the EPA values from the surveys (F, P, and A) shown. The diagonal terms are the correlation values we have can find the correlation between the three dimensions from the survey data are shown in (b), and the correlation between estimated three dimensions observe both tables the values in tables (b) and (c) are close. It reveals that BERTNN estimation is highly correlated with survey data and the cross-term dynamics is well estimated.
carlier in Table 3 Also, you
shown in (c). and survey results. We can
One problem with estimation from shallow- embeeddnns was having the same embedding for words identity or behavior. Using the last two layers of the BERT model as embedding, can differ-
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Table 4: Comparing estimated affective meanings for "judge" an identity and behavior.
Judge
Evaluation
Potency
Activity
Est. Evaluation
Est. Potency
Est. Activity
Behavior
-1.83
0.71
0.07
-2.03
0.98
0.24
Identity 1.15
2.53
-0.22
1.57
1.56
-0.42
Table 5: Correlation analysis for identities. (a) Correlation between estimated values (EA, EP, and EA) and the affective dictionary value (E, P, and A). We can also compare the correlation of the words in the three dimensions shown in (b) and correlation of the estimated values of the three dimension shown in (c) to find how close the off-diagonal entries are in the estimation comparing
dictionary values.
(a)
E
P
A
(b)
E
P
A
(c)
EF
EP
EA
EE
0.93
0.57
0.06
E
1.00
0.55
0.05
EE
1.00
0.62
0.06
EP
0.57
0.93
0.31
P
0.55
1.00
0.25
EP
0.62
1.00
0.33
EA
0.04
0.25
0.93
A
0.05
0.25
1.00
EA
0.06
0.33
1.00
entiate between these two cases. In Table 4 we can observe that estimated values for "judge" are different when it considered identity behavior.
Tables 5 and show that diagonal terms in the correlation of estimated values and values from the survey dictronary are reasonably large. On the other hand, the off-diagonal entries from the estimation are close to the ones from the survey.
5 CONCLUDING REMARKS
big limitation the applicability of ACT to different contexts such as NLP tasks was the limitation of affective vocabulary collected from surveys. In this paper, approach make training and test sentences that describe an ABMO event. Then we used BERT as an embedding and fine-tuned it using a neural network and regression model. Our approach, BERTNN, resulted in state of the in estimating affective meaning.
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Areej Alhothali and Jesse Hoey. Semi-supervised affective meaning lexicon expansion using se- mantic and distributed word representations. arXiv preprint arXiv:/703: 170.00925, 09825, 2017.
Christine Averett and David R Heise. Modified social identities: Amalgamations, attributions, and emotions. Journal of Mathematical Sociology, 13(1-2):103-132, 1987.
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Table 6: Correlation analysis for behaviors. (a) Correlation between estimated values (EA, EP. and EA) and the affective dictionary value (E, and A). We can also compare the correlation of the words in the three dimensions shown in (b) and correlation of the estimated values of the three dimension shown in (c) find how close the off-diagonal entries are the estimation comparing to
dictionary values.
(a)
E
P
A
(b)
E
(c)
EE
FP
EA
EE
0.94
0.55
-0.23
E
1.00
0.59
-0.21
EE
1.00
0.63
-0.26
EP
0.59
0.86
0.13
P
0.59
1.00
0.12
0.63
1.00
0.12
EA
-0.25
0.11
0.93
A
-0.21
0.12
1.00
EA
-0.26
0.12
1.00

---

Under review as a conference paper at ICLR 2022
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10


Reviews:
[{"review_id": "OrJJaieyWE", "reviewer": "Reviewer_4E5D", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths and Weakness:\n(1) The authors tackle the problem of extending affective vocabulary through a fine-tuning approach and overcoming the drawbacks of the traditional approach of vocabulary collected from surveys. Results obtained from the paper shows that the fine-tuned model achieves shows a high correlation between the estimated values and affective dictionary values for identities and behavior\n(2) The method is pretty simple for fine-tuning the BERT model and is not explained properly. The authors also provide no information about the parameters used for training which makes it impossible to reproduce the results of the paper. \n(3) The evaluation section is pretty weak and there are no baselines or other approaches that are being compared for this task.\n(4) The data processing and preparing the training and test set to describe an ABO event is unclear and needs to be explained properly.\n(5) The authors make a lot of claims that are unsubstantiated in this paper especially about extending the affective dictionaries. There are no results to demonstrate those. ", "summary_of_the_paper": "In this paper, the authors propose using BERT embedding as an alternative to shallow word embeddings to find affective lexicons and also show that fine-tuning the model achieves state-of-the-art in estimating affective meaning.", "summary_of_the_review": "In this paper, the authors fine-tune BERT to find affective lexicons and show high correlations between estimated values and dictionary values. However, the paper is filled with a lot of drawbacks from methodology, style of writing, and results. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "HSSamErTJg", "reviewer": "Reviewer_UkRH", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\nThe idea of using a pre-trained model to  aid in finding a sentiment lexicon based on ACT theory is interesting.\n\nWeaknesses:\n\nThe experimental analysis is clearly insufficient.：\n-The data set used for the experiments was not presented.\n-The parameters involved in the methods, such as learning rate, batch-size, etc., are not described\n- No relevant work of others was cited.\n-The analysis about the experimental results are too superficial.\n\nThe figures and tables of the article obviously need to be redrawn, e.g. Table 2 and Figure 4.\n", "summary_of_the_paper": " This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for finding affective lexicons based on ACT theory.", "summary_of_the_review": "This paper contains too much background knowledge up front, while the experimental part is too little and the analysis is too superficial.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "DELU9ZqAX3-", "reviewer": "Reviewer_Ketb", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This paper proposes a new sentiment representation method by using Affect Control Theory (ACT) and BERT model. Using ACT could capture the social interactions and sentiment changes. The main contribution of this paper is the usage of ACT and the whole process. According to the experimental results, the BERT model shows good performance.\n\n+Ves\n+ The usage of Affect Control Theory is very interesting and the first work.\n+ The paper is a nice effort with visualizations and nice structure. \n\n-Concerns\n\n-The ablation studies and some experiments should be expanded. For instance, changing different numbers of NN layers, adding other word embedding comparative experiments to show the effectiveness of BERT model.\n\n-Many experimental details are insufficient, such as parameters.\n\n-How to use this model to extend affective dictionaries? The authors should add some examples or explain clearly.\n\n-For the input dataset, how to distinguish the different categories of words, like identity or behavior.\n\n-In Method, the authors use One affective dictionary, which dictionary, please give the reference.\n\n-When a word is polysemous, like apple (fruit or company), how to do in this model.\n \nMinor comments: \n\n*The figures are a bit blurry, please replace them with vector diagrams.\n", "summary_of_the_paper": "This paper proposes a new sentiment representation method by using Affect Control Theory (ACT) and BERT model. Using ACT could capture the social interactions and sentiment changes. The main contribution of this paper is the usage of ACT and the whole process. According to the experimental results, the BERT model shows good performance.", "summary_of_the_review": "The idea is interesting and results show the effectiveness. However, something is not clear and some ablation studies are missing.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "7gHggHJnV8k", "reviewer": "Reviewer_cj4C", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Comments on Methodology:\n\nFirstly, the authors state they use a regression model to finetune the output of their neural network, the details of which are unclear. The authors need to elaborate on how finetuning was done with the model.\n\nSecondly, the methodology suggested in the paper looks relatively straightforward in the following sense:  embeddings from BERT were used in a feed-forward neural network, and certain aspects of this were finetuned.  \n\nFinally, a few gaps that could strengthen the paper quite a bit are left out. For instance, the authors could also compare the model's performance using shallow word embeddings as well as contextual embeddings from advanced transformer-based architectures. They could further include the data preprocessing and implementation details for the experiments in a separate section.\n\nComments on Writing:\n\nThe grammar and sentence structure of many sentences in the paper could be significantly improved. Below, a few grammatical errors and sentences from the paper are listed for reference below.\n\n - Page 3, Line 5: \"following two ones\" should be \"following two\"\n\n - Page 5, Line 8: \"But we have to feet the dataset so that it can understand in which category we are working.\" The context of this sentence is unclear. \n\n - Page 5, Line 10: \"To use BERT as a contextual word-embedding, we should have the following processing.\" \nThe sentence may need rewording (aren't the embeddings derived from the BERT model?). \n\n - Page 7, Line 2: \"In this paper, an approach to make training and test sentences that describe an ABO event.\" This line is incomplete.\n\n - please be consistent in wording (e.g., data-set vs dataset) ", "summary_of_the_paper": "In this paper, the authors propose a deep learning architecture to estimate the affective meaning of words in terms of their EPA (Evaluation, Potency, and Activity) scores. Their methodology achieves notable performance improvements over the approaches in the existing literature, and this is due to the contextual embeddings derived from the BERT model.", "summary_of_the_review": "There are clear gaps in both the proposed ideas and the writing/presentation of results.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This work presents a new sentiment representation method with the use of affect control theory and BERT. Reviewers pointed out several major concerns towards the insufficient experiments and results, as well as the lack of ablation studies and related work discussion. I would like to encourage the authors to take into account the comments from reviewers to further improve their work for a stronger version for future submissions.
Decision: Reject