Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

A social network of crime : A review of the use of social networks for crime and the detection of crime

Social media is used to commit and detect crimes. With automated methods, it is possible to scale both crime and detection of crime to a large number of people. The ability of criminals to reach large numbers of people has made this area subject to frequent study, and consequently, there have been several surveys that have reviewed specific crimes committed on social platforms. Until now, there has not been a review article that considers all types of crimes on social media, their similarity as well as their detection. The demonstration of similarity between crimes and their detection methods allows for the transfer of techniques and data between domains. This survey, therefore, seeks to document the crimes that have been committed on social media, and demonstrate their similarity through a taxonomy of crimes. Also, this survey documents publicly available datasets. Finally, this survey provides suggestions for further research in this field

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

DESIGN OF PEOPLE PROFILING AND MODELING REPUTATION COMPUTATION BASED ON SENTIMENT ANALYSIS

The number of popular people is still growing because of the easiness to access information technology. Every time people upload things and let people watch it and give it a like or comment. People who can impress other people will grow their popularity and fame. Some famous people make influences, help poor people with powers, and others are causing troubles. Community these days drives people perspective by share their thoughts on social media. They spread information and makes others want to see things they are talked about. Troublesome popular people defended by their fan base and attacked by other communities. By these cases, the research tried to gather information on social media and used it for calculation and profiling. The method that proposed to rely on this information is based on sentiment analysis to look up someone’s record and listing them into top 10 best got from DBpedia. This system shows the list of people and contains all important record about that person which can be used for decision support for a policy or rewarding people. The results have successfully visualized the output in the list of people with any further details following by clicking their names

SENTIMENT AND BEHAVIORAL ANALYSIS IN EDISCOVERY

A suspect or person-of-interest during legal case review or forensic evidence review can exhibit signs of their individual personality through the digital evidence collected for the case. Such personality traits of interest can be analytically harvested for case investigators or case reviewers. However, manual review of evidence for such flags can take time and contribute to increased costs. This study focuses on certain use-case scenarios of behavior and sentiment analysis as a critical requirement for a legal case’s success. This study aims to quicken the review and analysis phase and offers a software prototype as a proof-of-concept. The study starts with the build and storage of Electronic Stored Information (ESI) datasets for three separate fictitious legal cases using publicly available data such as emails, Facebook posts, tweets, text messages and a few custom MS Word documents. The next step of this study leverages statistical algorithms and automation to propose approaches towards identifying human sentiments, behavior such as, evidence of financial fraud behavior, and evidence of sexual harassment behavior of a suspect or person-of-interest from the case ESI. The last stage of the study automates these approaches via a custom software and presents a user interface for eDiscovery teams and digital forensic investigators

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research