Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in Twitter Data

2017 ◽  
Vol 13 (4) ◽  
pp. 89-108 ◽  
Author(s):  
Santosh Kumar Bharti ◽  
Ramkrushna Pradhan ◽  
Korra Sathya Babu ◽  
Sanjay Kumar Jena
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The Other ◽  
High Pitch ◽  
Intended Meaning ◽  
Twitter Data ◽  
Textual Data ◽  
Sentiment Detection

In Natural Language Processing (NLP), sarcasm analysis in the text is considered as the most challenging task. It has been broadly researched in recent years. The property of sarcasm that makes it harder to detect is the gap between the literal and its intended meaning. It is a particular kind of sentiment which is capable of flipping the entire sense of a text. Sarcasm is often expressed verbally through the use of high pitch with heavy tonal stress. The other clues of sarcasm are the usage of various gestures such as gently sloping of eyes, hands movements, shaking heads, etc. However, the appearances of these clues for sarcasm are absent in textual data which makes the detection of sarcasm dependent upon several other factors. In this article, six algorithms were proposed to analyze the sarcasm in tweets of Twitter. These algorithms are based on the possible occurrences of sarcasm in tweets. Finally, the experimental results of the proposed algorithms were compared with some of the existing state-of-the-art.

scholarly journals Natural Language Processing in Serious Games: A state of the art.

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Davide Picca ◽  
Dominique Jaccard ◽  
Gérald Eberlé
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Serious Games ◽  
State Of The Art ◽  
Serious Game ◽  
The Other ◽  
Other Hand ◽  
The One ◽  
High Level

In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure:  first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.

scholarly journals The Semantics and Collocations Relation in Food Reviews

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Fazel Keshtkar ◽  
Ledong Shi ◽  
Syed Ahmad Chan Bukhari
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Topic Modeling ◽  
State Of The Art ◽  
Semantic Relation ◽  
The Other ◽  
Good Place ◽  
The Common

Finding our favorite dishes have became a hard task since restaurants are providing more choices and va- rieties. On the other hand, comments and reviews of restaurants are a good place to look for the answer. The purpose of this study is to use computational linguistics and natural language processing to categorise and find semantic relation in various dishes based on reviewers’ comments and menus description. Our goal is to imple- ment a state-of-the-art computational linguistics meth- ods such as, word embedding model, word2vec, topic modeling, PCA, classification algorithm. For visualiza- tions, t-Distributed Stochastic Neighbor Embedding (t- SNE) was used to explore the relation within dishes and their reviews. We also aim to extract the common pat- terns between different dishes among restaurants and reviews comment, and in reverse, explore the dishes with a semantics relations. A dataset of articles related to restaurant and located dishes within articles used to find comment patterns. Then we applied t-SNE visual- izations to identify the root of each feature of the dishes. As a result, to find a dish our model is able to assist users by several words of description and their inter- est. Our dataset contains 1,000 articles from food re- views agency on a variety of dishes from different cul- tures: American, i.e. ’steak’, hamburger; Chinese, i.e. ’stir fry’, ’dumplings’; Japanese, i.e., ’sushi’.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Random Multiple Choice Questions Generation using Nlp

2020 ◽  
Vol 9 (9) ◽  
pp. 395-397
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Resource Utilization ◽  
Language Processing ◽  
Multiple Choice ◽  
Teaching Staff ◽  
Multiple Choice Questions ◽  
Textual Data

Students’ life is incomplete without exams because exams are those that help students in evaluating themselves and thus proceeding further in studies. So, the starting step in conducting such examinations is creating a question paper. Generating a question paper is still in its traditional way, where lecturers or professors that are the teaching staff are doing it manually and wasting a terrible amount of time in selecting what type of questions are to be generated. It’s so difficult to create a question paper as it includes a lot of resource utilization and exhaustion. These tasks can be automated. As we are seeing a lot of development in new, exciting technologies and these technologies can help and can make the process of automation easier. So for automation, we use Machine Learning and Natural Language Processing as this whole task involves using and manipulating textual data. In this solution, we provide our model with a textual paragraph from which the questions are to be selectively generated and we develop the multiple choices using a certain distinctive process for the users.

Textual entailment graphs

2015 ◽  
Vol 21 (5) ◽  
pp. 699-724 ◽  
Author(s):  
LILI KOTLERMAN ◽  
IDO DAGAN ◽  
BERNARDO MAGNINI ◽  
LUISA BENTIVOGLI
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
State Of The Art ◽  
Text Analytics ◽  
Joint Work ◽  
Gold Standard Dataset ◽  
Textual Entailment ◽  
Interesting Task

AbstractIn this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

Research on Sustainable Mining Engineering

2013 ◽  
Vol 340 ◽  
pp. 126-130 ◽  
Author(s):  
Xiao Guang Yue ◽  
Guang Zhang ◽  
Qing Guo Ren ◽  
Wen Cheng Liao ◽  
Jing Xi Chen ◽  
...  
Keyword(s):  
Information Processing ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Economy ◽  
Scientific Research ◽  
The Other ◽  
Mining Engineering ◽  
Chinese Information Processing ◽  
Development Tendency

The concepts of Chinese information processing and natural language processing (NLP) and their development tendency are summarized. There are different comprehension of Chinese information processing and natural language processing in China and the other countries. But the work appears to emerge in the study of key point of languages processing. Mining engineering is very important for our country. Though the final task of languages processing is difficult, Chinese information processing has contributed substantially to our scientific research and social economy and it will play an important part for mining engineering in our future.

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