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Ting Zhang and Changxiu Cheng
The public?s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability duri...
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Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner and Markus Schedl
Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the f...
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Xuanyu Zhang, Hao Zhou, Ke Yu, Xiaofei Wu and Anis Yazidi
In Natural Language Processing (NLP), deep-learning neural networks have superior performance but pose transparency and explainability barriers, due to their black box nature, and, thus, there is lack of trustworthiness. On the other hand, classical mach...
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Ashokkumar Palanivinayagam, Claude Ziad El-Bayeh and Robertas Dama?evicius
Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely...
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Shelley Gupta, Archana Singh and Vivek Kumar
Virtual users generate a gigantic volume of unbalanced sentiments over various online crowd-sourcing platforms which consist of text, emojis, or a combination of both. Its accurate analysis brings profits to various industries and their services. The sta...
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Zohreh Madhoushi, Abdul Razak Hamdan and Suhaila Zainudin
Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there ar...
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Najla Alkaabi, Nazar Zaki, Heba Ismail and Manzoor Khan
Students? emotional health is a major contributor to educational success. Hence, to support students? success in online learning platforms, we contribute with the development of an analysis of the emotional orientations and triggers in their text message...
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Maria Krommyda, Anastasios Rigos, Kostas Bouklas and Angelos Amditis
Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opini...
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Yun Peng and Jianmei Wang
This study aims to explore the time series context and sentiment polarity features of rumors? life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural networ...
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Lazaros Vrysis, Nikolaos Vryzas, Rigas Kotsakis, Theodora Saridou, Maria Matsiola, Andreas Veglis, Carlos Arcila-Calderón and Charalampos Dimoulas
Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against ref...
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Mustafa Coskun and Meltem Ozturan
The growth and popularity of social media platforms have generated a new social interaction environment thus a new collaboration and communication network among individuals. These platforms own tremendous amount of data about users? behaviors and sentime...
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Xing Liu, Fupeng Wei, Wei Jiang, Qiusheng Zheng, Yaqiong Qiao, Jizong Liu, Liyue Niu, Ziwei Chen and Hangcheng Dong
Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The...
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Guowei Li, Fuqiang Lin, Wangqun Chen and Bo Liu
Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using con...
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Krzysztof Fiok, Waldemar Karwowski, Edgar Gutierrez, Tameika Liciaga, Alessandro Belmonte and Rocco Capobianco
Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is n...
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Alireza Alaei, Ying Wang, Vinh Bui and Bela Stantic
Social media have been a valuable data source for studying people?s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedente...
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Ranjan Satapathy, Shweta Rajesh Pardeshi and Erik Cambria
In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment ana...
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Richard G. Mayopu, Yi-Yun Wang and Long-Sheng Chen
Recent studies have indicated that fake news is always produced to manipulate readers and that it spreads very fast and brings great damage to human society through social media. From the available literature, most studies focused on fake news detection ...
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Ahmed Al Marouf, Jon G. Rokne and Reda Alhajj
Detecting societal sentiment trends and emotion patterns is of great interest. Due to the time-varying nature of these patterns and trends this detection can be a challenging task. In this paper, the emotion patterns and trends are detected among social ...
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Barakat AlBadani, Ronghua Shi and Jian Dong
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance...
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Wei Jiang, Yandong Wang, Zhengan Xiong, Xiaoqing Song, Yi Long and Weidong Cao
The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly f...
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