This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

https://hal.archives-ouvertes.fr/hal-01838537/document

SemEval-2016 task 5 : aspect based sentiment analysis

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

/pdf/semeval-2017-task-4-sentiment-analysis-in-twitter-5294l6x1f7.pdf

SemEval-2017 Task 4: Sentiment Analysis in Twitter

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

Deep learning for sentiment analysis: A survey

Thispaper overviews 18 plagiarism detectors that have been developed and evaluated within PAN'10. We start with a unified retrieval process that sum- marizes the best practices employed this year. Then, the detectors' performances are evaluated in detail, highlighting several important aspects of plagiarism de- tection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year's competition.

/pdf/overview-of-the-2nd-international-competition-on-plagiarism-30hy5ipx0c.pdf

Overview of the 2nd International Competition on Plagiarism Detection

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.

/pdf/automatic-sarcasm-detection-a-survey-4c50j2tj3v.pdf

Automatic Sarcasm Detection: A Survey

Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parsetrees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neural Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art textbased methods for sarcasm detection, yielding an F-score of .92.

/pdf/fracking-sarcasm-using-neural-network-59z7wpcark.pdf

Fracking Sarcasm using Neural Network

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.

/pdf/semeval-2015-task-11-sentiment-analysis-of-figurative-3lv4em4fgn.pdf

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker’s mindset. Insight into a speaker’s psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture, we show significant gains in detection accuracy when knowledge of the speaker’s mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.

/pdf/magnets-for-sarcasm-making-sarcasm-detection-timely-18jz3lia3q.pdf

Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal

This paper reports about our work in the ICON 2009 NLP TOOLS CONTEST: Parsing. We submitted two runs for Bengali. A statistical CRF based model followed by a rule-based post-processing technique has been used. The system has been trained on the NLP TOOLS CONTEST: ICON 2009 datasets. The system demonstrated an unlabeled attachment score (UAS) of 74.09%, labeled attachment score (LAS) of 53.90% and labeled accuracy score (LS) of 61.71% respectively.

/pdf/dependency-parser-for-bengali-the-ju-system-at-icon-2009-1tcfuyevkp.pdf

Dependency Parser for Bengali: the JU System at ICON 2009

This paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, “Detection and interpretation of English Puns”. Our system consists of two probabilistic models for each type of puns using Google n-gram and Word2Vec. Our system achieved f-score of calculating, 0.663, and 0.07 in homographic puns and 0.8439, 0.6631, and 0.0806 in heterographic puns in task 1, task 2, and task 3 respectively.

/pdf/idiom-savant-at-semeval-2017-task-7-detection-and-3fd6l4gapt.pdf

Aniruddha Ghosh

Papers

Fracking Sarcasm using Neural Network

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal

Dependency Parser for Bengali: the JU System at ICON 2009

Idiom Savant at Semeval-2017 Task 7: Detection and Interpretation of English Puns