#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets.

n/

DEVELOPMENT OF SCHEDULING SYSTEM WITH GENETIC ALGORITHM IN WEBSITE-BASED SMK NEGERI 1 SINE

Scheduling is an information that has limited conditions that must be met. Preparation of the schedule will take quite a long time if it is done using conventional media such as writing on paper or books. Scheduling optimization is needed to provide effectiveness and efficiency so that the implementation of learning activities can run more optimally. The genetic algorithm approach method is used to get the optimum schedule. This algorithm produces the best combination for subject pairs and teaching teachers as a whole by determining the initial population and initializing the chromosomes, determining the fitness value, then carrying out crossover selection, and carrying out mutations to produce the best fitness value which will be used to determine the final value of scheduling. The results of the entire algorithm process are consistent with the original prediction data, and the same teacher is not scheduled to teach more than once at the same time. The results of the subject scheduling process using the genetic algorithm obtain a fairly good optimization in subject scheduling

Performance Improvement of Business Process Similarity Calculation using Word Sense Disambiguation

Similarity calculation between Business Process Models (BPM) has an important role in the process of managing BPM repository. One of its uses is to facilitate the searching process of a model in the repository. Similarity calculation between business processes is closely related with semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database, such as WordNet, to find the semantic meaning of words. The problem in WordNet is that this lexical database contains terms wich have more than one meaning or polysemous. Selecting the wrong meaning will  decrease the accuracy of similarity calculation process. In this study, we will try to improve the accuracy of similarity calculation of business processes using Word Sense Disambiguation (WSD). The main purpose is to eliminate the ambiguity of polysemous words before calculating the similarity value. WSD is performed by unsupervised methods based on the value of graph connectivity. Then, we used a lexical database that is focused in the business and industry field. The results from this study is able to achieve higher accuracy of the sense selection process for terms especially terms that are related to business and industrial domains. It will also increase the accuracy of similarity value calculation between the business process models

B-BabelNet: Business-Specific Lexical Database for Improving Semantic Analysis of Business Process Models

Similarity calculation between business process models has an important role in managing repository of business process model. One of its uses is to facilitate the searching process of models in the repository. Business process similarity is closely related to semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database such as WordNet to find the semantic meaning of the word. The activity name of the business process uses terms that specifically related to the business field. However, most of the terms in business domain are not available in WordNet. This case would decrease the semantic analysis quality of business process model. Therefore, this study would try to improve semantic analysis of business process model. We present a new lexical database called B-BabelNet. B-BabelNet is a lexical database built by using the same method in BabelNet. We attempt to map the Wikipedia page to WordNet database but only focus on the word related to the domain of business. Also, to enrich the vocabulary in the business domain, we also use terms in the business-specific online dictionary (businessdictionary.com). We utilize this database to do word sense disambiguation process on business process model activity’s terms. The result from this study shows that the database can increase the accuracy of the word sense disambiguation process especially in particular terms related to the business and industrial domains

Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure

Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence related to the veracity of the rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.Comment: To appear in Proceedings of the 2nd International Workshop on Rumours and Deception in Social Media (RDSM), co-located with CIKM 2018, Turin, Italy, October 201

cross domain and cross lingual abusive language detection a hybrid approach with deep learning and a multilingual lexicon

n/

Penggunaan N-gram pada Analisa Sentimen Pemilihan Kepala Daerah Jakarta Menggunakan Algoritma Naïve Bayes

In the current era of globalization of the Internet is very rapid development, because the human need for the internet is always evolving and rapid technological advancement. Most people use the internet to access social media, one of which is social media twitter. Many people who express their wishes or opinions on social media twitter is both positive and negative opinions. Opinions from this community can be used as research to obtain an information. The result of such information in its utilization requires proper analysis so as to provide support in determining a decision. Sentiment analysis is a data processing technique that can solve the problem well. Sentiment analysis was used in this study to see the opinion of the public against the election of the Jakarta regional head on social media twitter. This study used the Naïve Bayes algorithm to classify opinions to be positive or negative by using the selection of features of Chi Square that have been done N-gram before. The purpose of this research is to see the level of classification accuracy using Naïve Bayes algorithm by using N-gram feature