In the sentiment analysis task, predicting the sentiment tendency of a sentence is an important branch. Previous research focused more on sentiment analysis in English, for example, analyzing the sentiment tendency of sentences based on Valence, Arousal, Dominance of sentences. the emotional tendency is different between the two languages. For example, the sentence order between Chinese and English may present different emotions. This paper tried a method that builds a domain-specific lexicon. In this way, the model can classify Chinese words with emotional tendency. In this approach, based on the [13], an ultra-dense space embedding table is trained through word embedding of Chinese TikTok review and emotional lexicon sources(seed words). The result of the model is a domain-specific lexicon, which presents the emotional tendency of words. I collected Chinese TikTok comments as training data. By comparing The training results with the PCA method to evaluate the performance of the model in Chinese sentiment classification, the results show that the model has done well in Chinese. The source code has released on github:https://github.com/h2222/douyin_comment_dataset
Beijing University of Posts and Telecommunications（北京邮电大学）
With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context fe…
Although, the fair amount of works in sentiment analysis (SA) and opinion mining (OM) systems in the last decade and with respect to the performance of these systems, but it still not desired performance, especially for morphologically-Rich Language (MRL) such as Arabic, due to the complexities and challenges exist in the nature of the languages itself. One of these challenges is the detection of idioms or proverbs phrases within the writer text or comment. An idiom or proverb is a form of speech or an expression that is peculiar to itself. Grammatically, it cannot be understood from the indiv…
In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market doma…
There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine…
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）
Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic commenting system requires large parallel corpora. Although part of articles are naturally paired with the comments on some websites, most articles and comments are unpaired on the Internet. To fully exploit the unpaired data, we completely remove the need for parallel data and propo…
TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）
Heidelberg University（海德堡大学）；Sofia University（索菲亚大学）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，HBKU）
We transfer a key idea from the field of sentiment analysis to a new domain: community question answering (cQA). The cQA task we are interested in is the following: given a question and a thread of comments, we want to re-rank the comments so that the ones that are good answers to the question would be ranked higher than the bad ones. We notice that good vs. bad comments use specific vocabulary and that one can often predict the goodness/badness of a comment even ignoring the question, based on the comment contents only. This leads us to the idea to build a good/bad polarity lexicon as an anal…
The Tone of Awareness: Topic, Sentiment, and Toxicity Maps During Mental Health Month on TikTok
Institute for Biocomputation and Physics of Complex Systems (BIFI)（生物计算与复杂系统物理研究所）；University of Zaragoza（萨拉戈塔大学）；ARAID Foundation（ARAID基金会）；Network Science Institute（网络科学研究所）；Northeastern University London（伦敦东北大学）；Kent Medway Medical School（肯特梅德斯医疗学院）；LASIGE（拉西格研究所）；Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院）；Department of Psychology, University of Limerick（利默里克大学心理学系）；Observatory on Social Media, Indiana University（社交媒体观察所，印第安纳大学）；CSSI - Kellogg School of Management, Northwestern University（CSSI - 北western大学凯洛格管理学院）
A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon
Ramakrishna Mission Vivekananda Educational and Research Institute（罗摩克里希那传道会辨喜教育与研究学院）
Institute of Computing and Information Technology, Gomal University（戈马尔大学计算与信息技术学院）
Rapid increase in internet users along with growing power of online review sites and social media has given birth to sentiment analysis or opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. This paper proposed sentence-level lexical based domain independent sentiment classification method for different …
Vrije Universiteit Brussel（布鲁塞尔自由大学）；Belgian Royal Military Academy（比利时皇家军事学院）；Open University of the Netherlands（荷兰开放大学）；University of Groningen（格罗宁根大学）
University of Southern California（南加州大学）；Caltech（加州理工学院）
Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job, thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events t…
Product-related reviews are based on users' experiences that are mostly shared on videos in YouTube. It is the second most popular website globally in 2021. People prefer to watch videos on recently released products prior to purchasing, in order to gather overall feedback and make worthy decisions. These videos are created by vloggers who are enthusiastic about technical materials and feedback is usually placed by experienced users of the product or its brand. Analyzing the sentiment of the user reviews gives useful insights into the product in general. This study is focused on three smartpho…
Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate grades or 2) utilized aspect extraction to craft a review form from what students actually communicated. Our work utilizes, rather than discards, student data from review form comments to deliver better information to the instructor. In this work, we detail the process by which we…
The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, an…
University of Southern Queensland（南昆士兰大学）；Lingnan University（岭南大学）
Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, (1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and …
Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting
Freie Universität Berlin（柏林自由大学）；Fraunhofer FOKUS（弗劳恩霍夫研究所（FOKUS））；MSB Medical School Berlin（柏林医学学校（MSB））；funk - Content-Netzwerk ARD / ZDF（ARD/ZDF内容网络）；InfAI Leipzig（莱比锡InfAI）
Shahjalal University of Science and Technology（沙贾拉尔科技大学）
Social media sites such as YouTube and Facebook have become an integral part of everyone's life and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media websites faces a variety of challenges including small imbalanced data sets, the findings of an appropriate model and also the choice of feature analysis method. further more, this problem is more severe for the Bengali speaking community due to the lack of gold standard labelled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowd …
In this paper, we design an integrated algorithm to evaluate the sentiment of Chinese market. Firstly, with the help of the web browser automation, we crawl a lot of news and comments from several influential financial websites automatically. Secondly, we use techniques of Natural Language Processing(NLP) under Chinese context, including tokenization, Word2vec word embedding and semantic database WordNet, to compute Senti-scores of these news and comments, and then construct the sentimental factor. Here, we build a finance-specific sentimental lexicon so that the sentimental factor can reflect…
