Sarcasm Detection in Text: Design Document
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1 CSC Senior Design Project Specification Professor Jie Wei Wednesday, November 23, 2016 Sarcasm Detection in Text: Design Document Jesse Feinman, James Kasakyan, Jeff Stolzenberg
2 1 Table of contents Overview 2 Literature Review 3 Contextualized Sarcasm Detection on Twitter 3 Overview 3 Dataset 3 Features 4 Tweet features 4 Author features 4 Audience features 4 Response features 5 Results 5 Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon 6 Overview 6 Datasets 6 Twitter Dataset 6 Amazon Dataset 7 Classification Algorithm 7 Results 9 Design outline 9 Dataset 10 Classification 11 N-Gram Frequency Classification 11 Suffixes 11 Term Frequency-Inverse Document Frequency 12 Sentiment Analysis 12 N-Gram Sentiment 12 Capitalization, Punctuation, Hashtags, and Emoji 12 Hashtags and Emoji 13 Hashtags 13 Emoji 13 Long words and vowel-less words 14 Pattern Collection and Matching 15 Part of speech patterns 15
3 2 GloVe 15 N-Grams 16 Patterns 16 Part of Speech Patterns 16 Context 16 N-fold Cross-Validation 17 Neural Network 17 References 18 Overview This document outlines our design methodology for building a classification model to detect sarcasm in text. First, in the literature review section, we review recent attempts by professionals in the field to tackle the problem of modeling sarcasm, and examine their methodologies and the machine learning techniques used in their models. We review two papers by Bamman and Smith [1] and Davidov et. al [2]. We then describe the design methodology we intend to follow for our approach to the problem, drawing on the techniques in the referenced papers but also expanding upon them with our own ideas.
4 3 Literature Review Contextualized Sarcasm Detection on Twitter Overview This paper [1] written in 2015 by David Bamman and Nolan Smith, professors of Computer Science at Carnegie Mellon University, attempts to tackle the problem of building a classification model to detect sarcasm in tweets. Because of the unique structure of tweets, they are able to gather data that is both pre-labeled as sarcastic, and that contains information about the context of the text. Dataset When considering the source for their dataset, Bamman and Smith noted that in previous attempts to design systems to classify sarcasm the datasets were labeled by human judges who were prone to error, claiming they found low agreement rates between human annotators at the task of judging the sarcasm of others tweets [1]. They also noted that previous attempts to model sarcasm treated it as a text categorization problem, while they felt that sarcasm requires shared knowledge between speaker and audience; it is a profoundly contextual phenomenon [1]. For this reason, Bamman and Smith wanted to capture contextual features for their model. To achieve these goals, they crawled through the last 3,200 tweets of all tweet authors between a nine month period spanning From this set, they took 9,767 tweets that were replies to another tweet (context), and that contained at least three words and had #sarcastic or
5 4 #sarcasm as their final term. For the negative sample, they examined tweets during that same time period that were not self labeled with #sarcastic or #sarcasm. This yielded a balanced training set with 9,767 self labeled sarcastic tweets, and 9,767 non self labeled tweets. Features Features were divided into four classes according to the type of information they captured. The four classes were tweet features, author features, audience features, and response features. Tweet features Tweet features are those derived completely from the text of the tweet to be predicted. These include binary indicators of unigrams and bigrams, as well as binary indicators of unigrams and bigrams in a reduced 1000 Brown cluster space. Part of speech features like ratio of nouns to verbs and density of hashtags or emoticons are also included as tweet features, as well as capitalization features, and both tweet level and word level sentiment features. Author features Author features are derived from information about the user who wrote the tweet to be predicted. These include binary indicators of the top 100 terms in the author corpus scored by TF-IDF. Bamman and Smith note that this is the single most informative feature, where a binary logistic regression classifier scores an accuracy of 81.2% when trained only on this feature. Other author features include profile information like gender and number of followers, as well as historical author sentiment features. Audience features Audience features attempt to capture information about the shared context between the author of the tweet to be predicted, and the author of the tweet being replied to. These included all the
6 5 features listed above as author features, but computed for the author of the original tweet that was replied to by the author of the tweet being predicted. Features that capture the historical communication between these two users like number of previous messages sent are also included as audience features. Response features Response features are derived from information about the contents of the original and reply tweets. These include binary indicators of pairwise Brown features between the two tweets, as well as binary indicators of unigrams in the original tweet. Results Bamman and Smith trained binary logistic regression models on all possible combinations of features. Using only tweet level features, their model achieved an average accuracy of 75.4% across 10 fold cross validation. Adding response features increased the accuracy of the model by under 2% to 77.3%, and combining tweet level features with audience features increased accuracy by 3.6% to 79.0%. Combing tweet features and author features provided the largest jump in accuracy, going from 75.4% using only tweet features to 84.9% when combining tweet and author level features. This is just.2% lower than the accuracy of a model trained on all features, which scored an accuracy of 85.1%. From these results, Bamman and Smith conclude that capturing context is vital for models that attempt to predict sarcasm, since the features design to capture context provide significant improvements in accuracy over tweet level features alone.
7 6 Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon Overview This paper [2] written in 2010 by Dmitry Davidov, Oren Tsur, and Ari Rappoport, PhD students at The Hebrew University, focuses on using a semi-supervised approach to sarcasm identification. This experiment was performed on two very different data sets, the first being a set of tweets from Twitter and the second a collection of Amazon reviews. Utilizing sentences that were ranked and pre-labeled based on level of sarcasm, the team constructed feature vectors that were in turn used to build a classifier model that assigned scores to unlabeled examples. Datasets Twitter Dataset The first dataset that this team utilized came from Twitter. Twitter is a very popular microblogging service. It allows users to publish and read short messages called tweets. Tweets are restricted to 140 characters and may contain references to url addresses, references to other Twitter users (these appear and content tags (called hashtags) assigned by the tweeter (#). Due to Twitter s informal nature and its constraint on character length the team found that users are often forced to use a large amount of slang, shortened lingo, ascii emoticons and other tokens absent from formal lexicons. The three experimenters stated that These characteristics make Twitter a fascinating domain for NLP applications, although posing great challenges due
8 7 to the length constraint, the complete freedom of style and the out of discourse nature of tweets [2]. The Twitter dataset that was used was comprised of 5.9 million unique tweets. In this dataset, the average number of words per tweet was Additionally, 18.7% of the tweets contained a url, 35.5% contained a reference to another twitter user, and 6.9% contained at least one hashtag. Amazon Dataset The second dataset that was used in this experiment was a collection of reviews from Amazon.com. This dataset contained 66,000 reviews of 120 different products found on Amazon. The reason the researchers selected this dataset was because of its stark contrast to the Twitter dataset. The Amazon reviews averaged 953 characters which are much longer than the tweets. They were more structured and grammatical than tweets and are delivered in a known context. Classification Algorithm The algorithm used by this team of researchers was semi-supervised. The input was a small seed of labeled sentences that had been annotated by three humans. The annotated sentences were ranked on a scale from 1 to 5 in which a score of a 5 indicted a clearly sarcastic sentence and a score of a 1 indicated a clear absence of sarcasm. Once the team had the labeled sentences they extracted a set of features to be used in feature vectors. The main feature types that were utilized were syntactic and pattern based features. Feature vectors for each of the labeled examples in the training set were constructed and used to build a classifier model that assigned scores to the unlabeled examples. Data Preprocessing
9 8 The first aspect of the framework for the algorithm this team used was the preprocessing of the data. To facilitate pattern matching the team had specific information replaced with meta data tags. Each appearance of a product, author, company, book name, user, url, and hashtag were replaced with the following corresponding generalized tags: [PRODUCT], [COMPANY], [TITLE], [AUTHOR], [USER], [LINK] and [HASHTAG]. Pattern Extraction The main feature type for the algorithm was based on surface patterns. The team classified words into two types. The first type was high frequency words (HFW) for words with a frequency greater than 1,000 words per million. The second type were content words (CW) for words with a frequency of less than 100 words per million. A pattern was then defined as an ordered sequence of 2-6 HFW s and 1-6 CW s [2]. Pattern Matching Once patterns are identified, a single entry was constructed in the feature vectors for each sentence. A feature value was then calculated for each pattern. An exact match to a sentence labeled sarcastic in the training set scored a 1. Sparse and Incomplete matches scored slightly lower respectively. Sentences with no pattern matches scored a zero. Additional Features In addition to pattern-based features some generic features were used as well. These included the sentence length in words, the number of exclamation point! characters in the sentence,
10 9 the number of question mark? characters in the sentence, the number of quotes in the sentence, and the number of capitalized words in the sentence. Classification Lastly the team needed to assign scores to the new examples in the test set. To do this they use a k-nearest neighbors (knn)-like strategy. Feature vectors were constructed for each example in the training and test sets. For each feature vector v in the test set, they computed the Euclidean distance to each of the matching vectors in the extended training set, where matching vectors share at least one pattern feature with v. The score was then a weighted average of the k closest training set vectors. Results The experiment conducted by these three PhD students yielded promising results. The researchers found that on average, the semi-supervised algorithm achieved a precision of 77% and a recall of 83.1%. They were surprised to find that punctuation marks served as the weakest indicator for sarcasm. However, the use of excessive exclamation marks and capital letters were moderately useful sarcasm indicators. The use of three consecutive dots which when combined with other features constituted a strong predictor. Design outline Our methodology to detect sarcasm is based upon the research we have done. Going step by step, from simpler to more advanced classification methods, we will evaluate the efficacy of
11 10 each using cross validation. The end result will be a system that uses a variety of classification tools and which we have eliminated the classifiers which produced no benefit or hurt the results. Dataset The data we will gather to do the project with will likely be live streaming tweets that we gather for a period of a few days or weeks until we have a sufficiently large amount to train our system with. However twitter data is problematic, while the data is readily available there is little to no context due to the short messages. In order to gain context with twitter data we need to look at replies, past tweets, and the user s profile. While these may be possible it is a more advanced option that we hope to be able to get to by the end of the project, but using and gathering the context for each tweet is more of a stretch goal. Ideally we will be able to attain a data set that has more context in the surrounding text and does not require specific background of the actors. To narrow the scope, using twitter data initially, we will select #sarcasm, #sarcastic, etc as well as other hashtags that allow us to tailor our system to a specific niche area to focus upon. This focus will make our system less generalizable but, in theory, be more accurate with that particular data set. If we are able to attain a sufficiently high accuracy with a niche focus then testing the system on a more general data selection would be the next goal. Our data will be tagged as sarcasm or not sarcasm so we will be using primarily supervised learning techniques. Tweets will require pre-processing to remove erroneous hashtags and replace proper nouns with generics so that we are analyzing the sarcasm of the language not of the subject of the message. We can also process the tweets with the proper nouns included in case subject matter expertise yields better results.
12 11 Classification We will be implementing multiple systems to attempt to classify sarcasm. Each system will return a normalized value between 0 to 1 which can then be processed by a neural network. Using python s scipy and nltk libraries we will create classifiers that indicate on a scale of 0 to 1 with 0 being entirely non-sarcastic and 1 being completely sarcastic, what the confidence of an individual classifier s results are. N-Gram Frequency Classification We plan to explore using n-gram frequencies with different sizes of n. By looking at words and phrases that are common in sarcastic remarks we hope to be able to train the system to recognize and be able to classify sarcastic remarks. Creating 2 frequency tables, 1 for sarcasm and 1 for non-sarcasm then comparing the frequency of a given n-gram to the frequency tables. Returning the percent match to each of the tables. We would then repeat the n-gram frequency analysis with a lemmatized version of the message to see if there s a difference in results. Suffixes Using the same technique as n-gram frequency analysis we would also create a frequency analysis using the suffixes of words in the tweets by lemmatizing the words and subtracting the
13 12 lemmatized word from the original word. For the same reasoning as n-gram frequency analysis, we hope to discover patterns in suffix frequency that can help classify sarcastic remarks. Term Frequency-Inverse Document Frequency With the same n-grams above, we will also look at the frequency of each n-gram and its relation to the inverse frequency of that term in the corpus of all messages we are looking at. This may tell us the importance of certain terms and we may be able to draw trends towards some words indicating sarcasm or not sarcasm. This is mainly useful on the tweets that have proper nouns left in them to see if certain people, places, or things indicate sarcasm. Sentiment Analysis We are going to look into the sentiment of each message we are going to analyze. If we are able to determine a trend in the sentiment that can help us classify sarcastic remarks then we will include full message sentiment analysis. We will be using the Minqing Hu and Bing Liu s sentiment word list to train the sentiment system that is provided in NLTK. N-Gram Sentiment Whole message sentiment may not be very revealing but if we look at partial sentiment, looking at n-gram sentiment of various sizes, we hope to be able to identify a trend in sarcastic remarks that can help us better classify sarcasm. Capitalization, Punctuation, Hashtags, and Emoji Most of the time with NLP we would make everything the same case and not pay much attention to punctuation. In our case we will attempt to find patterns in capitalization and punctuation that can help us determine sarcasm classifications. For instance text that appears
14 13 in quotes may be treated differently than text that is surrounding the quoted message or multiple exclamation and question marks may indicate a different meaning than a message without them. This analysis will be difficult as it can be done both with the context of the words that are surrounded by relevant punctuation or without the given word and just looking at the punctuation patterns. Hashtags and Emoji With social media being more involved many users use hashtags and emoji to provide implicit information that helps the reader understand the true intent of their message. Hashtags We will remove the obvious hashtags such as sarcasm, serious, and anything that can be used to definitively identify a message as sarcasm but we will try and analyze the remaining hashtags that are present to see if theres trends in hashtags that can elude to the sarcastic or serious nature of a remark. Emoji There are 2 types of emoji that we will be looking at, the first is strings of non word characters such as :) : ( >_<, etc which are supposed to represent faces and express an emotion towards the topic that s discussed. These will not necessarily be easy to identify and may require us to compile a database of existing ones prior to analyzing, then looking for them in messages and using frequency analysis and patterns to try and identify trends. The second type are single character emoji that are often used by users from the emoji selection on mobile keyboards. These come in as single characters, usually a unicode identifier,
15 14 that uniquely represents that symbol. Attempting to analyze the emoji by itself would be difficult and would be futile given that they render differently on different systems. However we will take the character identifier and use frequency and pattern analysis to try and derive the meaning of the emoji as it relates to sarcastic remarks given the context in which it occurs in our training data set. For instance, if a positive sentiment message is followed by a particular emoji: Yay, Trump Won! Ύ To us it s clear that the message writer is not happy because that face is usually associated with disappointment. WIth enough examples in a training set of emoji being used in context we hope to be able to establish their meaning. Additionally we have the option to pull a database of existing emoji and the words used to describe them then assign them with sentiments based on the database. Using these sentiments we could substitute the emoji for the given sentiment or for a synonym to the emotion it s intended to mean. Long words and vowel-less words Looking at words with a large number of syllables and words that don t have vowels has shown to be a possible method of sarcasm detection that we are going to explore. Looking at the frequency of vowel-less words in a message and the frequency of messages with large numbers of syllables may help us in our classification.
16 15 Pattern Collection and Matching We will attempt to find phrase patterns that occur in sarcastic remarks but not, or far less frequently, in non sarcastic remarks. Patterns are n-grams which have generic values for some of the words. For instance I went to the [generic]. would be a pattern. Then by checking with the common words from our previous analysis, check if that pattern matches in the message and if it does, if the generic term is high in our sarcasm frequency table. Part of speech patterns Previous research has suggested that the parts of speech and the frequency, density, and patterns of those parts may be another useful tool. Using N-gram and pattern matching analysis on the POS tags of a message we hope to be able to extract useful classification information. GloVe The research we looked at used Brown clustering to establish context however we will be using Global Vectors for Word Representation or GloVe to plot words in multidimensional vector space based upon their context due to the more advanced nature of GloVe we are expecting better results. When vectors are closer to one another in this space it means they have a more similar context, which for us may indicate that words which occur in certain contexts are more likely to be sarcastic or not.
17 16 N-Grams Using the n-grams we previously looked at, we intend to compute the n-grams position in multidimensional space with GloVe based on the context in which the n-grams. This can allow us to draw similarities to the context of the n-grams we found to the trained n-grams to see if there s context that s similar to sarcastic remarks. Patterns Using the same technique for the n-grams above, we would look for the context in which the patterns occur in multidimensional space based on their context. By looking at the language patterns that are previously discussed to see if certain patterns occur in concert with other patterns that may indicate sarcasm. Part of Speech Patterns In the same way both the n-grams and patterns are checked in multidimensional space, we would, too, check the part of speech patterns against this space. Context This is not as practical for tweets given the inherent lack of contextual information that they provide however if possible, we would like to be able to analyze the surrounding sentences to a potentially sarcastic remark then use that context to help drive our decision when classifying the potentially sarcastic phrase. Using the sentiment and the subjects of the preceding and succeeding sentence we may be able to establish that a given sentence is sarcastic.
18 17 N-fold Cross-Validation With the various techniques we will be using to try and detect sarcasm we need to be able to analyze which are working the best and why. Using cross validation on the various classifiers we will use will help to validate the results of each individual tool and to allow us to fine tune each one. Neural Network The number of individual classifiers we are going to try to explore is quite large so in order to try and appropriately balance how much weight each classifier should get, besides basic hard coded cross validation, we will create a neural network using the Scikit-learn library s MLP Classifier that can take the outputs of all these classifiers and integrate their outputs into a fully connected neural network and adjust the weights within the network to give us optimal results in as many circumstances as possible.
19 18 References [1] D. Bamman and N. A. Smith, Contextualized Sarcasm Detection on Twitter in International Conference on Web and Social Media, [2] D. Davidov, O. Tsur, and A. Rappaport, Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon in Proceedings of the Fourteenth Conference on Computational Natural Language Learning, 2010, pp
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