Proceedings Article•
Sarcasm as Contrast between a Positive Sentiment and Negative Situation
01 Oct 2013-pp 704-714
TL;DR: This work develops a sarcasm recognizer that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets and shows that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition.
Abstract: A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as “love” or “enjoy”, followed by an expression that describes an undesirable activity or state (e.g., “taking exams” or “being ignored”). We have developed a sarcasm recognizer to identify this type of sarcasm in tweets. We present a novel bootstrapping algorithm that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets. We show that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition.
Citations
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01 Apr 2017
TL;DR: A survey on hate speech detection describes key areas that have been explored to automatically recognize these types of utterances using natural language processing and discusses limits of those approaches.
Abstract: This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.
1,030 citations
Cites background from "Sarcasm as Contrast between a Posit..."
...computational approaches can actually solve these problems or whether hate speech is a research problem similar to sarcasm where only certain subtypes have been shown to be automatically detected with the help of NLP (Riloff et al., 2013)....
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...Below we list other terms that are used in the NLP community....
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...Unlike other tasks in NLP, hate speech may have strong cultural implications, that is, depending on one’s particular cultural background, an utterance may be perceived as offensive or not....
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...It remains to be seen, whether in the future new computational approaches can actually solve these problems or whether hate speech is a research problem similar to sarcasm where only certain subtypes have been shown to be automatically detected with the help of NLP (Riloff et al., 2013)....
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...It is mainly aimed at NLP researchers who are new to the field of hate speech detection and want to inform themselves about the state of the art....
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TL;DR: It is argued that data statements will help alleviate issues related to exclusion and bias in language technology, lead to better precision in claims about how natural language processing research can generalize and thus better engineering results, protect companies from public embarrassment, and ultimately lead to language technology that meets its users in their own preferred linguistic style.
Abstract: In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development. Through the adoption and wi...
620 citations
Cites background from "Sarcasm as Contrast between a Posit..."
...The latter includes annotations for sentiment (Liu, 2012) and for figurative language or sarcasm (e.g., Riloff et al., 2013; Ptáček et al., 2014)....
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Book•
01 Jun 2015TL;DR: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.
Abstract: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
587 citations
TL;DR: A divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type, which shows that sentence type classification can improve the performance of sentence-level sentiment analysis.
Abstract: A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.
586 citations
Cites background from "Sarcasm as Contrast between a Posit..."
...Riloff et al. (2013) presented a bootstrapping alorithm for sarcasm recognition that automatically learned lists f positive sentiment phrases and negative situation phrases from arcastic tweets....
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01 Sep 2016
TL;DR: This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
531 citations
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
"Sarcasm as Contrast between a Posit..." refers methods in this paper
...We used the LIBSVM (Chang and Lin, 2011) library to train two support vector machine (SVM) classifiers: one with just unigram features and one with both unigrams and bigrams....
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06 Oct 2005
TL;DR: A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.
Abstract: This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify the contextual polarity for a large subset of sentiment expressions, achieving results that are significantly better than baseline.
3,433 citations
10 May 2005
TL;DR: A novel framework for analyzing and comparing consumer opinions of competing products is proposed, and a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews.
Abstract: The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.
1,758 citations
"Sarcasm as Contrast between a Posit..." refers background or methods in this paper
...Liu05 : A positive and negative opinion lexicon from (Liu et al., 2005)....
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...We experimented with three resources: Liu05 : A positive and negative opinion lexicon from (Liu et al., 2005)....
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15 Mar 2011
TL;DR: This work wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs.
Abstract: Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, — a “sentiment lexicon” or “affective word lists”. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength.
848 citations
"Sarcasm as Contrast between a Posit..." refers background in this paper
...AFINN11 The AFINN sentiment lexicon designed for microblogs (Nielsen, 2011; Hansen et al., 2011) contains 2,477 manually labeled words and phrases with integer values ranging from -5 (negativity) to 5 (positivity)....
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Proceedings Article•
01 Jun 2013TL;DR: This work systematically evaluates the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy on Twitter and achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks.
Abstract: We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging is improved from 90% to 93% accuracy (more than 3% absolute). Qualitative analysis of these word clusters yields insights about NLP and linguistic phenomena in this genre. Additionally, we contribute the first POS annotation guidelines for such text and release a new dataset of English language tweets annotated using these guidelines. Tagging software, annotation guidelines, and large-scale word clusters are available at: http://www.ark.cs.cmu.edu/TweetNLP This paper describes release 0.3 of the “CMU Twitter Part-of-Speech Tagger” and annotated data. [This paper is forthcoming in Proceedings of NAACL 2013; Atlanta, GA, USA.]
780 citations
"Sarcasm as Contrast between a Posit..." refers methods in this paper
...We applied CMU’s part-of-speech tagger designed for tweets (Owoputi et al., 2013) to this data set....
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