ICWSM 2010 Blog

The Hebrew University researchers developed a semi-supervised algorithm called SASI to identify sarcastic sentences in online product reviews. The study, which involved 66,000 Amazon product reviews, achieved a 77% precision and 83.1% recall rate in identifying sarcasm. The algorithm also identified key linguistic patterns and features defining sarcastic utterances New York State Divorce Laws Division of Property.
 

The ICWSM paper on sarcasm in the news sheds light on how language nuances impact communication. A Mobile App Development Company in Seattle can create tools to analyze sentiment, helping businesses better understand and react to news and user interactions.
 

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The paper "A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Product Reviews" by Oren Tsur, Dmitry Davidov, and Ari Rappoport is creating quite a buzz, with coverage from CNET, Financial Post, and others. This cutting-edge research focuses on using semi-supervised techniques to identify sarcasm in product reviews, a challenging but crucial task for improving sentiment analysis and natural language processing.

The paper will be presented at ICWSM during Wednesday's Language Analysis session, so be sure to check it out. For more details and related content, check in modbibo for additional resources and insights!
 

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Sarcasm detection in news headlines has been a topic of interest for researchers. A dataset curated by Rishabh Misra includes 28,000 news headlines, with 13,000 labeled as sarcastic. The dataset includes headlines from The Onion and HuffPost, allowing models to distinguish between sarcastic and non-sarcastic headlines. Deep learning approaches have been employed to detect sarcasm in news headlines, such as the GMP-LSTM Model, a hybrid model combining Long Short-Term Memory (LSTM) and Global Max Pooling layers, and Evidenceential Deep Learning with LSTM and GRU networks. Other studies have used machine learning classifiers, such as Voted Classification, which uses a bag-of-words model with term frequency and n-gram analysis, followed by a voted classifier combining multiple classifiers. These studies contribute to understanding and detection of sarcasm in news headlines, employing various datasets and machine learning techniques. While not directly published in ICWSM proceedings, they offer valuable insights into the application of sarcasm detection in news media.als stem cell treatment germany
 

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OMG this paper is chef’s kiss 👏 The way it breaks down sarcasm in news like it's decoding some next level di sini file got me hooked. Honestly, reading this felt like finding the hidden .apk inside serious journalism layered, coded, and kinda hilarious if you get it. Lowkey wish all academic stuff hit this hard 😂