Sentiment Symposium Tutorial: Language and cognition

  1. Overview
  2. A pervasive, challenging phenomenon
  3. Affect and emotion
  4. Style and social meaning
  5. Meaning and use
  6. Perspective
  7. Commitment and linguistic structure
  8. Non-literal language
  9. Summary of conclusions


This section provides a brief overview of results from linguistics and cognitive psychology concerning the dimensions of affectivity and the ways in which attitudinal and emotional information is expressed.

A pervasive, challenging phenomenon

Communication depends crucially on information about speaker attitudes and perspectives, and hearer inferences about that information.

"Sam bought that damn bike."

Our understanding of what is conveyed by damn in this (or any) case depends on what we know about the speaker, the hearer, and their relationship, their goals and preferences, and the information they have already exchanged about the topic in question. That is, apprehending the sentiment is as deep a problem as cognitive science has to offer.

As a result, sentiment analysis is hard. We haven't yet discussed the models and techniques involved in the results of figure fig:others, but the picture is intuitively clear: whereas classifying spam messages or picking out traditional topics is a largely solved problem, sentiment analysis results typically do not exceed 90% accuracy for realistic problems. (Where the reported figures are better than this, you might ask about the underlying class distribution, over-fitting, and out-of-domain robustness.)

Figure fig:others
A single classifier model (MaxEnt) applied to three different domains at various vocabulary sizes. panglee is the widely used movie review corpus distributed by Lillian Lee's group. The 20 newsgroups corpus is a collection of newsgroup discussions on topics like sports, religion, and motorcycles, each with subtopics. spamham is a corpus of spam and ham email messages.

Part of the challenge is that sentiment information is blended and multidimensional. We try to approximate it as a classification problem, but this is often inappropriate. I think no social networking site brings out this fact more clearly than the Experience Project, where users upload stories about themselves that are often impossible to categorize, since they involve a wealth of different kinds of sentiment information (and elicit correspondingly complex reactions from readers). Table tab:ep provides a few examples of texts that really ought not to be classified along any single emotional dimension.

Table tab:ep
Emotionally blended 'confessions' from the Experience Project.
I have a crush on my boss! *blush* eeek *back to work*
I fell over in the playground this afternoon, in front of all the parents and kids. I fell backwards over a cone. It was pretty funny, but now my neck hurts like crazy.
When I left my workplace today I kinda felt like I had to pee. But I was in rush to go home and figured it would be ok. So I have to take the bus and then walk for about another 15 minutes to get home. By the time I got on to the bus I knew I really had to pee. I was sitting down and had my legs tightly crossed and pretty much in agony scared of peeing myself in front of all the people in the bus. [...]
I really hate being shy ... I just want to be able to talk to someone about anything and everything and be myself.. That's all I’ve ever wanted.

Affect and emotion

I think computational sentiment analysis is not yet at the point where it can decide between cognitive theories of emotion or affect, but existing theories can be very useful when analyzing data, particularly for:

Scherer's (1984) typology of affective states (figure fig:affect) provides a broad framework for understanding sentiment. In particular, it helps to reveal that emotions are likely to be just one kind of information that we want our computational systems to identify and characterize.

Figure fig:affect
Scherer's typology of affective states.

Plutchik's (2002) Wheel of Emotions (figure fig:wheel) is a proposal for how to relate emotions to one another. It's also helpful for the color scheme it provides, which can inform visualization choices.

Figure fig:wheel
Plutchik's Wheel of Emotions.

Ekman's (1985) theory of emotions is based in facial expressions but is intended to have broad applicability; see figure fig:ekman.

Table fig:ekman
Ekman's six basic emotions, as expressed by the facial muscles: surprise, happiness, anger, fear, disgust, sadness.

It's often important to distinguish author from reader perspectives, as these are likely to elicit different emotions. Some examples are given in table tab:reactions. Of course, we might see many other combinations as well, but these are prominent on social networking sites.

Table tab:reactions
Likely pairings of author states and reader reactions.
Author's story ofElicited reader reaction
sadnesssympathy and solidarity

Style and social meaning

What linguists discuss under the rubric of social meaning includes a lot of Scherer's interpersonal stance and emotional traits.

Speaking in the social world involves a continual analysis and interpretation of categories, groups, types, and personae and of the differences in the ways they talk — in social cognition terms, a development of schemata (Piaget 1954). These emerge as we come to notice differences, to make distinctions, and to attribute meaning to them. Thus we construct a social landscape through the segmentation of the social terrain, and we construct a linguistic landscape through a segmentation of the linguistic practices in that terrain.

(Eckert 2008: 455)

Some topics that are of particular relevance to social media:

There is an additional layer of complexity: for some of these signals, we send them intentionally in order to convey extra meaning. Others just seep out because they are not under our control.

Meaning and use

A lot of sentiment information is highly context-dependent.

When I think about my own understanding of the words and phrases of my native language, I find that in some cases I am inclined to say that I know what they mean, and in other cases it seems more natural to say that I know how to use them.

(Kaplan 1999)

David Kaplan


Keeping track of perspectival information is essential for robust utterance understanding. This is particularly true for evaluative language, which is always interpreted from some kind of subjective position.

The following quotation from Lasersohn 2005 also points out how complex the relationship between sentiment and subjectivity/objectivity is, in that even our subjective claims are meant to have some broad applicability beyond our own preferences.

Our basic problem is that if John says This is fun and Mary says This is not fun, it seems possible for both sentences simultaneously to be true (relative to their respective speakers), but we also want to claim that John and Mary are overtly contradicting or disagreeing with each other [...]

Peter Lasersohn

A very complex shifting of perspectives (from Harris and Potts 2009):

While shopping at one of my local Apple stores the other day, I overheard an earnest conversation about safeguarding Mac computers against things like viruses and trojans. The customer and companion were new to Mac life and were convinced that they should be very worried about viruses. The Apple salesperson on the floor repeatedly assured them that they would not need extra antivirus protection for their Mac. The customer then argued that Symantec makes an antivirus program for Macs, therefore, it must truly be a credible threat, otherwise there would be no such products. Some antivirus products are even sold in Apple stores. I’ve heard similar arguments before: if companies like Symantec or McAfee make antivirus applications for the Mac, then Macs must truly be vulnerable somehow, somewhere. Steve Jobs and the rest of the Apple cronies must be lying.

From Antivirus Programs for Mac, Snake Oil or Public Service?

Commitment and linguistic structure

Most robust, fast computational models treat documents as bags of words. In some areas of sentiment, this is basically appropriate, because some sentiment words and expressions are not directly influenced by what is around them:

  1. That was fun :)
  2. That was miserable :(

In other cases, where the sentiment words are influenced by what is around them, a bag of words might nonetheless do the job:

  1. I stubbed my damn toe
  2. What's with these friggin QR codes?

However, the normal situation is for sentiment words to be influenced in complicated ways by the material around them:

  1. It was wonderful.
  2. He knows it is wonderful.
  3. It was not wonderful.
  4. No one found it to be wonderful.
  5. They said it would be wonderful, but they were wrong: it was awful!
  6. This "wonderful" movie turned out to be boring.

In linguistic semantics, these influences are analyzed in terms of semantic scope: in the interpreted structure of these sentences, the word wonderful is in the semantic scope of operators that variously negate its scalar meaning or allow the speaker to back off of the semantic meaning. Figure fig:scope begins to suggest how this can be analyzed in terms of the graphical structure of the underlying sentence.

Figure fig:scope
Syntactic structures. The sentiment word wonderful is influenced by the operators that take scope over it, which alter or weaken speaker/author commitment.
figures/tree1.png figures/tree1.png figures/tree1.png

Non-literal language

The effects of non-literal language are extremely challenging. Even humans are apt to get confused about the intended meaning of some expressions of this form. We can hope to approximate their effects with sentiment analysis systems, but they are still likely to be a leading cause of errors.

  1. Irony and sarcasm: Oh, this is just great!, as clear as mud
  2. Implicit comparison: He did a good job for a linguist.
  3. Hypallage: He's not exactly brilliant.
  4. Hyperbole: No one goes there anymore.

Summary of conclusions

Properties of sentiment that we should keep in mind when building systems, conducting experiments, and reporting results:

  1. Sentiment is blended and multidimensional.
  2. Author and reader stances often contain very different but related sentiment information.
  3. Sentiment is linguistically and socially complex: ideally, we would know who was talking, to whom, and why, before venturing a guess about their attitudes and emotions.
  4. Sentiment is context dependent: the same string might be sincere in one context and wryly ironic in another.