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Sentiment Analysis Using Deep Learning
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With the popularization of social media, a vast amount of textual content with additional geo-located and time-stamped information is directly generated by human every day. Both tweet meaning and extended message information can be analyzed in a purpose of exploration of public mood variations within a certain time periods. This paper aims at describing the development of the program for public mood monitoring based on sentiment analysis of Twitter content in Russian. Machine learning (naive Bayes classifier) and natural language processing techniques were used for the program implementation. As a result, the client-server program was implemented, where the server-side application collects tweets via Twitter API and analyses tweets using naive Bayes classifier, and the client-side web application visualizes the public mood using Google Charts libraries. The mood visualization consists of the Russian mood geo chart, the mood changes plot through the day, and the mood changes plot through the week. Cloud computing services were used in this program in two cases. Firstly, the program was deployed on Google App Engine, which allows completely abstracts away infrastructure, so the server administration is not required. Secondly, the data is stored in Google Cloud Datastore, that is, the highly-scalable NoSQL document database, which is fully integrated with Google App Engine.
The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems. SemEval-2016 is the tenth workshop in the series of International Workshops on Semantic Evaluation Exercises. The first three workshops, SensEval-1 (1998), SensEval-2 (2001), and SensEval-3 (2004), focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and the subsequent SemEval workshops evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval-2016 were proposed in 2015. SemEval-2016 was co-located with the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’2016) in San Diego, California. It included the following 14 shared tasks organized in five tracks: • Text Similarity and Question Answering Track – Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation – Task 2: Interpretable Semantic Textual Similarity – Task 3: Community Question Answering • Sentiment Analysis Track – Task 4: Sentiment Analysis in Twitter – Task 5: Aspect-Based Sentiment Analysis – Task 6: Detecting Stance in Tweets – Task 7: Determining Sentiment Intensity of English and Arabic Phrases • Semantic Parsing Track – Task 8: Meaning Representation Parsing – Task 9: Chinese Semantic Dependency Parsing • Semantic Analysis Track – Task 10: Detecting Minimal Semantic Units and their Meanings – Task 11: Complex Word Identification – Task 12: Clinical TempEval iii • Semantic Taxonomy Track – Task 13: TExEval-2 – Taxonomy Extraction – Task 14: Semantic Taxonomy Enrichment This volume contains both Task Description papers that describe each of the above tasks and System Description papers that describe the systems that participated in the above tasks. A total of 14 task description papers and 198 system description papers are included in this volume. We are grateful to all task organisers as well as the large number of participants whose enthusiastic participation has made SemEval once again a successful event. We are thankful to the task organisers who also served as area chairs, and to task organisers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. We also thank the NAACL 2016 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsor, the ACL Special Interest Group on the Lexicon (SIGLEX). The SemEval-2016 organizers, Steven Bethard, Daniel Cer, Marine Carpuat, David Jurgens, Preslav Nakov and Torsten Zesch
Актуальность данного исследования обусловлена необходимостью выявления сте- пени статистической взаимозависимости текстовых сообщений, представленных в интернете, и финансовых результатов организаций. Основной целью работы, в рамках данной статьи, является выявление закономерностей и характеристик текстовых сообщений, которые позволят различать успешные организации от организаций, которые прекратили свою деятельность на рынке услуг, или находятся в кризисном положении.
В настоящей статье представлены результаты оригинального исследования возможностей сентимент-анализа брендов как нового современного инструмента маркетинговых исследований. Авторы проанализировали упоминания о бренде iPad в российских журналистских блогах и микроблогах платформы Twitter. Предложен обобщенный алгоритм проведения сентимент-анализа брендов, состоящий из набора методов и рекомендаций по оценке и аналитической обработке данных в рамках рассматриваемой методологии.
Содержание статьи.
• Введение, или почему блогосфера становится полем для сбора маркетинговой информации • Что такое сентимент-анализ: категоризация основных подходов • Мировая практика проведения сентимент-анализа брендов в блогосфере • Трудности при проведении сентимент-анализа брендов • Описание исследования • Основные результаты исследования, выводы и рекомендации • Ограничения и направления будущих исследований