z-uo/qasper-squad huggingface dataset:
Paper: Harnessing Cognitive Features for Sarcasm Detection
Sarcasm, in general, has been the focus of research for quite some time. In one of the pioneering works jorgensen1984test explained how sarcasm arises when a figurative meaning is used opposite to the literal meaning of the utterance. In the word of clark1984pretense, sarcasm processing involves canceling the indirectly negated message and replacing it with the implicated one. giora1995irony, on the other hand, define sarcasm as a mode of indirect negation that requires processing of both negated and implicated messages. ivanko2003context define sarcasm as a six tuple entity consisting of a speaker, a listener, Context, Utterance, Literal Proposition and Intended Proposition and study the cognitive aspects of sarcasm processing.
Question: What is the best reported system?
Answer: Gaze Sarcasm using Multi Instance Logistic Regression.
Paper: Harnessing Cognitive Features for Sarcasm Detection
Sarcasm often emanates from incongruity BIBREF9 , which enforces the brain to reanalyze it BIBREF10 . This, in turn, affects the way eyes move through the text. Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts. This hypothesis forms the crux of our method for sarcasm detection and we validate this using our previously released freely available sarcasm dataset BIBREF8 enriched with gaze information.
Question: What kind of stylistic features are obtained?
Paper: Harnessing Cognitive Features for Sarcasm Detection
The database consists of 1,000 short texts, each having 10-40 words. Out of these, 350 are sarcastic and are collected as follows: (a) 103 sentences are from two popular sarcastic quote websites, (b) 76 sarcastic short movie reviews are manually extracted from the Amazon Movie Corpus BIBREF11 by two linguists. (c) 171 tweets are downloaded using the hashtag #sarcasm from Twitter. The 650 non-sarcastic texts are either downloaded from Twitter or extracted from the Amazon Movie Review corpus. The sentences do not contain words/phrases that are highly topic or culture specific. The tweets were normalized to make them linguistically well formed to avoid difficulty in interpreting social media lingo. Every sentence in our dataset carries positive or negative opinion about specific “aspects”. For example, the sentence “The movie is extremely well cast” has positive sentiment about the aspect “cast”.
Question: What traditional linguistics features did they use?
Paper: Harnessing Cognitive Features for Sarcasm Detection
The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i.e., positive/negative). Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not., to rule out the Priming Effect (i.e., if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 ). The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention. This is done by setting the task to polarity annotation (instead of sarcasm detection). (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not.
Question: What cognitive features are used?
Answer: Readability (RED),  Number of Words (LEN), Avg. Fixation Duration (FDUR), Avg. Fixation Count (FC), Avg. Saccade Length (SL), Regression Count (REG), Skip count (SKIP), Count of regressions from second half
to first half of the sentence (RSF), Largest Regression Position (LREG),  Edge density of the saliency gaze
graph (ED),  Fixation Duration at Left/Source
(F1H, F1S),  Fixation Duration at Right/Target
(F2H, F2S),  Forward Saccade Word Count of
Source (PSH, PSS),  Forward SaccadeWord Count of Destination
(PSDH, PSDS), Regressive Saccade Word Count of
Source (RSH, RSS),  Regressive Saccade Word Count of
Destination (RSDH, RSDS)
Paper: How we do things with words: Analyzing text as social and cultural data
In June 2015, the operators of the online discussion site Reddit banned several communities under new anti-harassment rules. BIBREF0 used this opportunity to combine rich online data with computational methods to study a current question: Does eliminating these “echo chambers” diminish the amount of hate speech overall? Exciting opportunities like these, at the intersection of “thick” cultural and societal questions on the one hand, and the computational analysis of rich textual data on larger-than-human scales on the other, are becoming increasingly common.
Question: What approaches do they use towards text analysis?
Answer: Modeling considerations:  the variables (both predictors and outcomes)  are rarely simply binary or categorical;  using a particular classification scheme means deciding which variations are visible,; Supervised and unsupervised learning are the most common approaches to learning from data;  the unit of text that we are labeling (or annotating, or coding), either automatic or manual, can sometimes be different than one's final unit of analysis.
Paper: How we do things with words: Analyzing text as social and cultural data
We typically start by identifying the questions we wish to explore. Can text analysis provide a new perspective on a “big question” that has been attracting interest for years? Or can we raise new questions that have only recently emerged, for example about social media? For social scientists working in computational analysis, the questions are often grounded in theory, asking: How can we explain what we observe? These questions are also influenced by the availability and accessibility of data sources. For example, the choice to work with data from a particular social media platform may be partly determined by the fact that it is freely available, and this will in turn shape the kinds of questions that can be asked. A key output of this phase are the concepts to measure, for example: influence; copying and reproduction; the creation of patterns of language use; hate speech. Computational analysis of text motivated by these questions is insight driven: we aim to describe a phenomenon or explain how it came about. For example, what can we learn about how and why hate speech is used or how this changes over time? Is hate speech one thing, or does it comprise multiple forms of expression? Is there a clear boundary between hate speech and other types of speech, and what features make it more or less ambiguous? In these cases, it is critical to communicate high-level patterns in terms that are recognizable.
Question: What dataset do they use for analysis?
Paper: How we do things with words: Analyzing text as social and cultural data
The next step involves deciding on the data sources, collecting and compiling the dataset, and inspecting its metadata.
Question: Do they demonstrate why interdisciplinary insights are important?
Paper: How we do things with words: Analyzing text as social and cultural data
Many scholars in the humanities and the social sciences work with sources that are not available in digital form, and indeed may never be digitized. Others work with both analogue and digitized materials, and the increasing digitization of archives has opened opportunities to study these archives in new ways. We can go to the canonical archive or open up something that nobody has studied before. For example, we might focus on major historical moments (French Revolution, post-Milosevic Serbia) or critical epochs (Britain entering the Victorian era, the transition from Latin to proto-Romance). Or, we could look for records of how people conducted science, wrote and consumed literature, and worked out their philosophies.
Question: What background do they have?
Paper: How we do things with words: Analyzing text as social and cultural data
After identifying the data source(s), the next step is compiling the data. This step is fundamental: if the sources cannot support a convincing result, no result will be convincing. In many cases, this involves defining a “core" set of documents and a “comparison" set. We often have a specific set of documents in mind: an author's work, a particular journal, a time period. But if we want to say that this “core" set has some distinctive property, we need a “comparison" set. Expanding the collection beyond the documents that we would immediately think of has the beneficial effect of increasing our sample size. Having more sources increases the chance that we will notice something consistent across many individually varying contexts.
Question: What kind of issues (that are not on the forefront of computational text analysis) do they tackle?