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The recent improvement in web technologies and increasing user-generated content (UGC) in Hindi on the web is the inspiration behind the sentiment analysis in the language. This UGC can turn out to be extremely helpful for researchers, governments and organization to learn what's on public mind, to make sound decisions. Sentiment Analysis is a natural language processing task that mine information from various content structures such as reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral. In this paper, the authors have tried to investigate the on-going research in the field of sentiment analysis, particularly in the Hindi language.

Many emerging social sites, famous forums, review sites, and many bloggers generate huge amount of data in the form of user sentimental reviews, emotions, opinions, arguments, viewpoints etc. about different social events, products, brands, and politics, movies etc. Sentiments expressed by the users has great effect on readers, political images, online vendors. So the data present in scattered and unstructured manner needs to be managed properly and in this context sentiment analysis has got attention at very large level. Sentiment analysis can be defined as organization of the text which is used to understand the mindsets or feelings expressed in the form of different manners such as negative, positive, neutral, not satisfactory etc. This paper explains the implementation and accuracy of sentiment analysis using Tensor flow and python with any kind of text data. It works on embedding, LSTM and Sigmoid layers and finds the accuracy of data in iterative manner for better result

In becoming a develop nation by 2020, Malaysia Government realized the need in providing affordable house to the public. Since Second Malaysia Plan, government has implemented various affordable housing projects and it continues until recent Malaysia Plan. To measure the effectiveness of the initiatives taken, public opinion is necessary. A social media platform has been seen as the most effective mechanism to get information on people's thought and feeling towards certain issues. One of the best ways to extract emotions and thoughts from what people post in social media is through Sentiment Analysis (SA). This paper will propose a new framework that focuses on the application of sentiment analysis to assist the decision maker in understanding the real voice of the public in regard to property industry in Malaysia. The framework will consist of two components; sentiment classification at feature/aspect level and sentiment visualization to show the results of the analysis.

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements. ; Xunta de Galicia; ED431G 2019/01 ; Xunta de Galicia; ED431C 2020/11 ; This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150)

Political debate - in its essence - carries a robust emotional charge, and social media have become a vast arena for voters to disseminate and discuss the ideas proposed by candidates. The Brazilian presidential elections of 2018 were marked by a high level of polarization, making the discussion of the candidates' ideas an ideological battlefield, full of accusations and verbal aggression, creating an excellent source for sentiment analysis. In this paper, we analyze the emotions of the tweets posted about the presidential candidates of Brazil on Twitter, so that it was possible to identify the emotional profile of the adherents of each of the leading candidates, and thus to discern which emotions had the strongest effects upon the election results. Also, we created a model using sentiment analysis and machine learning, which predicted with a correlation of 0.90 the final result of the election. ; FCT – Fundação para a Ciência e Tecnologia within the Project Scope: ...

In this paper, sentiment classification techniques are incorporated into the domain of political news from columns in different Turkish news sites. We compared four supervised machine learning algorithms of Naive Bayes, Maximum Entropy, SVM and the character based N-Gram Language Model for sentiment classification of Turkish political columns. We also discussed in detail the problem of sentiment classification in the political news domain. We observe from empirical findings that the Maximum Entropy and N-Gram Language Model outperformed the SVM and Naive Bayes. Using different features, all the approaches reached accuracies of 65% to 77%.

Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain. ; Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments. ; This work has been co-funded by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), by the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and by the Spanish Ministry of Economy and Competitiveness, projects SNOLA (TIN2015-71669-REDT), RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER). It has also been supported by the Spanish Ministry of Education, Culture and Sport, under a FPU fellowship (FPU016/00526). ; Publicado

The purpose of this article is to study one of the methods of social networks analysis – text sentiment analysis. Today, social media has become a big data base that social network analysis is used for various purposes – from setting up targeted advertising for a cosmetics store to preventing riots at the state level. There are various methods for analyzing social networks such as graph method, text sentiment analysis, audio, and video object analysis. Among them, sentiment analysis is widely used for political, social, consumer research, and also for cybersecurity. Since the analysis of the sentiment of the text involves the analysis of the emotional opinions expressed in the text, the first step is to define the term opinion. An opinion can be simple, that is, a positive, negative or neutral emotion towards a particular object or its aspect. Comparison is also an opinion, but devoid of emotional connotation. To work with simple opinions, the first task of text sentiment analysis is to classify the text. There are three levels of classifications: classification at the text level, at the level of a sentence, and at the aspect level of the object. After classifying the text at the desired level, the next task is to extract structured data from unstructured information. The problem can be solved using the five-tuple method. One of the important elements of a tuple is the aspect in which an opinion is usually expressed. Next, aspect-based sentiment analysis is applied, which involves identifying aspects of the desired object and assessing the polarity of mood for each aspect. This task is divided into two sub-tasks such as aspect extraction and aspect classification. Sentiment analysis has limitations such as the definition of sarcasm and difficulty of working with abbreviated words.

In becoming a develop nation by 2020, Malaysia Government realized the need in providing affordable house to the public. Since Second Malaysia Plan, government has implemented various affordable housing projects and it continues until recent Malaysia Plan.To measure the effectiveness of the initiatives taken, public opinion is necessary.A social media platform has been seen as the most effective mechanism to get information on people's thought and feeling towards certain issues. One of the best ways to extract emotions and thoughts from what people post in social media is through Sentiment Analysis (SA).This paper will propose a new framework that focuses on the application of sentiment analysis to assist the decision maker in understanding the real voice of the public in regard to property industry in Malaysia.The framework will consist of two components; sentiment classification at feature/aspect level and sentiment visualization to show the results of the analysis.

The aim of this work is to review a specific learning analytics method - sentiment analysis - in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture – LADEL (Learning Analytics Dashboard for E-Learning) - is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.

As websites, social networks, blogs, and online portals proliferate on the internet, authors are producing reviews, opinions, ideas, ratings, and feedback. The emotional content of this writer may be about things like books, people, hotels, items, studies, events, and so on. These emotions have great value for businesses, for governments, and for people. The majority of the writer-generated material requires the usage of text mining algorithms and sentiment analysis, even if this information is meant to be instructive. Sentiment Analysis is a technique in Natural Language Processing (NLP) that tries to identify and extract assessments communicated within a given text. This paper intends to execute different content handling strategies in NLP and use of Valence Aware Dictionary for Sentiment Reasoning (VADER) Model that is sensitive to both polarity (positive/negative) and intensity (strength) of emotion.