Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11% of the sarcastic tweets in our dataset. The sentence 'Love waking up at 3 am' is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rulebased and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof. * Equal Contribution. † The work was done when authors were doing their Masters at IIT-Bombay.
Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semisupervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman's rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.
Since the advent of Internet, and later on Social Media and E-commerce websites, people have started expressing themselves more and more online instead of on paper. This give businesses and organizations an opportunity to analyze views and interests of people in accordance with a set of provided keywords, time duration, geographic locations, age group and thus. Following the human nature of curiosity, collecting a selected set of opinions and sentiments and then topic-oriented analysis allows extraction of rich information that would help make smarter business decisions, political campaigns and better product consumption.
Agitate Analysis is one of the overall utilized examination on Subscription Oriented Industries to break down client practices to anticipate the clients which are going to leave the help understanding from an organization. It depends on Deep Learning techniques and calculations and become so significant for organizations in the present business conditions as acquiring another client’s expense is more than holding the current ones. The paper audits the significant examinations on Customer Churn Analysis on Telecommunication Industry in writing to introduce general data to perusers about the regularly utilized information mining techniques utilized, results and execution of the strategies and revealing an insight to additional studies. To stay up with the latest, contemplates distributed in most recent five years and basically most recent two years have been incorporated.
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