Sentiment Analysis

Musicology is a small discipline within the wide spectrum of human knowledge, yet it is already divided into various branches, each with its own societies, conferences, journals, jargons, degrees, prejudices, …, and jobs. Although they share their object of investigation-"the art of music as a physical, psychological, aesthetic, and cultural phenomenon", these branches very often ignore one another. Research in musicology is mostly a solitary task, as investigations, papers, and publications are commonly signed by single authors, in contrast with STEM disciplines where teamwork is the rule. This is in part the result of tradition-the "Musicological Toolbox"-but also the aftermath of the job market and financing programs. Large funding schemes such as the European Research Council (ERC) grants are becoming a major disruptive factor in many disciplines in the humanities, including musicology. Scholars in all fields now have the opportunity to build research teams, and most of their members receive their salaries to exclusively work on the project. In other words, we are starting to build what could be called a Musicology Lab, learning along the way how teamwork is reshaping and transforming the Musicological Toolbox, the look and feel of our discipline, the way we work as well as the way we publish and disseminate our results. This paper presents some of the key features of the ERC Didone project, one of its principal tasks being to create a digitally encoded corpus of some 3,000 arias in MusicXML format from about 180 musical settings of a small number of opera librettos by Pietro Metastasio. It focuses on some of the project's research tasks, emphasizing how the skills of a team of eighteen scholars with very different expertise-historical musicology, music theory and analysis, cultural history, librettology, archival research, music performance, music engraving, MIR, computer science, and statistical modeling-combine to explore the potential answer(s) to the main research question of the project: How are emotions expressed through music?

Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.

The popularity of the social web introduces opportunities for the recommender systems, whilst new challenges arise when semantic knowledge is integrated in the landscape. The large amount of opinions available from Web 2.0 are exploited here to improve recommendation techniques in a semantic context. The developed recommendation system matches the crawled opinions against tourist objectives within the DBpedia ontology. Following a natural language processing step in Gate, several metrics are employed to build a recommendation plan, and formal justification is provided in case of need.

Sentiments are mental attitudes and play an important role in forming opinions. Sentiment and emotion are interchangeably used, however have different meanings. The sentiment is a mental attitude or a thought influenced by emotion and has an important role in forming opinions and influencing future decisions of others. Employees are knowledgeable assets for any organization, and their sentiments about their organization, managers, co-workers, etc. create an opinion. The emotional well-being of employees has a direct relationship with the performance of the organization. The World Health Organization declared COVID-19 as a pandemic on March 11, 2020. Many countries declared lockdowns, with unimaginable restrictions to control the spread of this pandemic. This resulted in organizations swiftly adapting to work-fromhome methods, almost overnight. This paper studies the sentiments and emotional wellbeing of employees during the COVID-19 pandemic. The study was conducted undertaking a review of the literature and adopting a framework for sentiment analysis and emotional wellbeing. The study shows that employees have mixed sentiments of feeling excited, positive, anxious, angry, and negative. Most of the participants with positive and excited sentiments believe that this pandemic has created a challenge as well as an opportunity.

The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and
effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate
the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined
four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the
proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.

DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that produces and displays superheroes and super villains. To start a super hero-themed business, there are a number of business examples that can be used as references and can also be used to reap profits from the business, namely, rental of comics, selling merchandise, making clothing lines, making cosplay costumes, making superhero-themed foods, selling action figure. In an effort to start a super hero-themed business, especially the DC Universe theme, it is necessary to pay attention / listen to DC Universe consumers in Indonesia. Classification of opinions or sentiment analysis is one way to find out about a person or group of people towards certain products, services, issues or groups from various social media platforms and the internet. Twitter is one of the social media that is loved by the people of Indonesia. This research tries to utilize what was written by Twitter social media users or better known as a tweet. Tweets will be processed by text mining and processed again using the Naïve Bayes Classifier algorithm.

The Digital India initiative by the Government of India is an initiative by Shri Narendra Modi, the Prime Minister of India. Launched in the year 2015, the programme enhanced its scope to various digital services wings. In this study, the researcher attempted to identify public sentiments by studying their comments through Tweets. The researcher considered the Tweets by Digital India for a period of one year

We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge-in the form of seed words-to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that ...

Twitter is the popular and commonly used social networking platform because it permits users to express their thoughts, opinions about any item, and allows them to post comments or messages all around the world. Sentiment Analysis techniques are used to study and analyze these reviews or opinions. Sentiment analysis is a NLP technique that is used to express opinions into dif erent sentiments like positive, negative, and neutral. In this paper, we take Airline Dataset from Twitter and did sentiment analysis on that dataset using machine learning algorithms like SVM, Naïve Bayes and Random Forest. Sentiments are expressed in three categories positive, negative and neutral. Our dataset contains 11533 tweets and the dataset is not balanced. The performance of various machine learning algorithms is discussed in this paper

2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used including textual features, twitter specific features, lexicon-based features, Parts of Speech (POS) features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.

Tietokoneiden laskentatehon kasvu on tehnyt mahdolliseksi asioiden, tapahtumien ja ilmiöiden synty- ja leviämismekanismien analysoinnin aiempaa tarkemmin. Optimistisimmat ovat arvioineet, että tietoteknologia on synnyttämässä uutta tiedettä – sosiaalifysiikkaa (social physics), joka antaa uusia mahdollisuuksia ihmisten käyttäytymisen ja ilmiöiden leviämisen tutkimiseen (Pentland 2014). Uutta tutkimusta ja uusia analysointimenetelmiä tarvitaan, sillä sosiaalinen media on tehnyt monesta yksityisestä ja aiemmin piiloon jääneestä asiasta julkista. Ei olekaan yllättävää, että sosiaalinen analytiikka ja erityisesti tunteita ja mielipiteitä ilmentävien sisältöjen automaattinen tunnistaminen on tätä nykyä yksi nopeimmin kasvavista tietoteknologian sovellusalueista (Gartner 2014). Sosiaalisella analytiikalla viitataan väljästi menetelmiin, työkaluihin ja toimintatapoihin, joilla organisaatiot pyrkivät hyödyntämään virtuaalisista sosiaalisista suhteista kertyvää tietoa oman toimintansa kehittämisessä.

Sosiaalista analytiikkaa on muun muassa se, kun yritys monitoroi ja analysoi yhteisösivustojen keskusteluja, jotta se voi kohdentaa mainontaansa paremmin, tunnistaa piilevää kysyntää tai oppia asiakkaidensa tuote- ja palvelukokemuksista. Yritysten ohella sosiaalinen analytiikka tarjoaa myös julkishallinnolle lukemattomia käyttökohteita. Esimerkiksi kunnalle on arvokasta tietää, millaisessa sävyssä sen tekemisistä ja palveluista sosiaalisessa mediassa keskustellaan. Kuntalaisten mielipiteiden parempi ymmärtäminen antaa mahdollisuuden käynnistää toimenpiteitä ja tehdä ratkaisuja, jotka vastaavat kuntalaisten odotuksia. Erityisesti nuorten kohdalla sosiaalisen analytiikan avulla voidaan saada aikaan hyviä tuloksia, jos nuorille syntyy tunne siitä, että heidän tarpeensa on huomattu. Muodollisilla palautejärjestelmillä on tulevaisuudessakin paikkansa, mutta demokratia voi sitä paremmin, mitä luontevammin hallintokoneisto ja kansalaiset toisensa kohtaavat. Sosiaalinen analytiikka ei yksinään riitä ratkaisuksi, mutta se saattaa hyvinkin olla keskeinen osa sitä.

This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the customer's perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer's perspective) and in making the personalized buying decisions (from the buyer's perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.

The tragic tale of Prince Andrei and Natasha Rostov that is the centerpiece of Tolstoy’s masterpiece is a brilliant depiction of the fallibility of life, sublimity of love and serenity in death. Andrei was a middle-aged widower and Natasha was still in her teens when love happened to them. While Natasha’s parents welcomed their romance, Andrei’s father was scornful about the match of that ‘chit of a girl’ from the family of no fortune or rank of consequence. However, he gives in subject to the condition that Andrei should put it off for a year and stay abroad during that period. “And then if your love or passion or obduracy – whatever you choose – is still as great, marry!” says the old man.

This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play Store in a related work. Fine-granular opinions and the classification of their sentiment orientation were already available. The models build upon them to make predictions based on their polarity. Predicting star ratings is of importance to the sentiment analysis community because it can better be understood how customers subjectively rate products. Rating them consistently with corresponding written reviews, however, remains a difficult task for automated predictors. This paper sheds new light in that direction.

The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most conc...

The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which refers to the determination of the sentiment of texts, has been proposed as an intelligent solution. From a methodological point of view, the recent combination of words embedding and deep neural networks (DNNs) has become an effective approach for sentiment analysis. In Persian studies, formal corpuses such as Wikipedia dumps have been used for word embedding. The fundamental difference between formal and informal texts means that the vectors derived from formal texts in informal contexts such as social networks do not result in desirable accuracy. To overcome this drawback, in this paper, we provide a large integrated text corpus of several different sources of informal comments and we also utilize the Fasttext as the word embedding algorithm. In this research, we use Attention-based LSTM, which has been shown to perform more effectively compared to the similar methods in sentiment analysis for the English language. The proposed method is evaluated on the two Persian "Taaghche" and "Filimo" datasets collected in this paper. The experiments on the two Persian datasets prove that utilizing informal vectors in sentiment analysis and applying the attention model improves the prediction accuracy of the DNN in the sentiment analysis of Persian texts.

La reflexión zubiriana sobre el sentimiento, se encamina a dilucidar la esencia del sentimiento estético mediante tres pasos sucesivos: en primer lugar, establecer qué es un sentimiento; en segundo lugar, indicar cuál es la relación entre el sentimiento y la realidad; en tercer y último lugar, abordar en qué consiste el sentimiento estético. En nuestro estudio nos limitaremos a las dos primeras etapas, pues nos parece que, a pesar de sus penetrantes intuiciones, Zubiri no ha desvelado la esencia del sentimiento por falta de una tercera pregunta que debería añadirse a las dos primeras: ¿por qué el sentimiento supone una relación con la realidad? La respuesta a esta cuestión constituirá el objetivo de este breve ensayo.

The presented study conducts a real-time sentiment analysis of the public reaction towards the announcement of the Indian Union Budget 2020. On social media platforms, the general public vents their opinions regarding popular issues. A total of 6000 tweets were mined to gauge the quick response of the public sentiment about the union budget which was presented in the Indian Parliament on February 1, 2020, at 11 am. The instantaneous reaction of the general public in the form of tweets was mined during the budget announcement hours. A sentiment analysis based model was used to analyze the dataset. Subjectivity and polarity values obtained for the dataset were used to calculate the sentiment for individual tweets. The experiment produced an overall positive score of +149.3387 based on the Twitter user’s collective opinions.

This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.

This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network (CMA-MemNet). This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects. In order to fix the memory network’s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information. We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself. Meanwhile, unlike most recurrent neural network (RNN) long short term memory (LSTM), gated recurrent unit (GRU) models, we retain the parallelism of the network. We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6. Compared with some popular baseline methods, our model performs excellently.

This research investigates the relationship between political opinions expressed on Social Media and the political opinions of the public (electorate) on major political event. Specifically, on the 2016 UK EU Referendum vote. It looks to answer the question, are Social Media sites such as Twitter echo chambers of people with the same opinion or do they accurately represent the opinions of the public. The investigation includes a computer program that extracts a Big Data set of tweets from Twitter, then analyses them to find their binary sentiment using a democracy of multiple machine learning sentiment classifiers. This is then compared to a database of pollster data from a variety of pollsters to find any differences. It also investigates why there is or isn’t a difference in opinions between Twitter users and the public.

The Indonesian tourism industry continues to develop and has become the core of the nation economy. Indonesia is known for its wealth of natural beauty, which can be used as a potential for tourism business. Garut is a city in Indonesia known for its beach, mountains, and culinary arts. The purpose of this study is to create a priority map of tourist attractions that can be utilized by local governments. Sentiment analysis was used 413,175 netizen comments via the social media platforms Instagram and Google reviews. Data was collected from January 2018-February 2019. The results show that the number of positive comments is significantly higher than the number of negative comments. Beach tourism a serious priority; not only is it the most preferred tourist attraction, but also the type that gets the most negative comments. While the main problem for Garut Regency tourism is hygiene, garbage is either overlapping or scattered, preventing Garut from having all-around tourism charm instead of being superior only in the sector of natural beauty. The suggestions in this research can be used as proposals for improving and developing tourism to realize a dignified 'Garut Charm'.

Facebook, the most popular social media (SM) platform has penetrated every nook and corner of the world. SM is now treated as the 'fifth Estate', other than legislative, executive, judiciary, and mainstream media. The power of SM as a critique is widely acknowledged. Establishments are finding it difficult to deal with it at times. Due to its ease of usage and relative anonymity, the general public finds it very convenient to put across their viewpoints, even if it's against the establishment. Some establishments at times are at loggerheads with champions of freedom of speech including civil rights activists. SM has been used for propaganda, marketing, and awareness campaigns. In this paper, we are proposing to use this powerful tool towards social change. Through a case study, a detailed process is being proposed for using social media particularly Facebook as an an-ti-stereotyping tool. The response to an online survey, the outcome of opinion mining, and the enthusiastic response to our case study by the targeted audience validate our hypothesis that Facebook can be effectively utilized as an anti-stereotyping tool.

Social media, understood as a means of communication as well as a space to socialise and interact, has become a common source for people to look for health information. Rather than consulting a single, authoritative source, they usually want a variety of opinions. At the same time, people are often exposed to information about vaccination online without necessarily looking for it.

The associated amplification of risk and risk perception through social media, has led some countries and health authorities to start monitoring social media platforms in order to counter the spread of misinformation and rebuild public trust in vaccination.

The purpose of this systematic scoping review was to analyse the social media monitoring techniques and interventions with the following objectives:

- identify preferences: which social media platforms do users choose as a source of information on vaccination and how does that influence their perceptions of vaccination?
- identify different methods and tools for social media monitoring in the context of vaccination including their strengths and weaknesses.
review how such monitoring and information gathered from social media monitoring can be used to inform communication strategies around immunisation.
- identify the uses, benefits and limitations of social media as an intervention tool in relation to vaccination (i.e. how effective social media is as an intervention tool for increasing vaccination).

An extensive database search led to the inclusion of 115 scientific articles in the review, looking at the use of social media, social media monitoring and (public health) interventions.

The review results show for example that between 4 and 62% of various study populations in different countries use social media as a source of information on vaccination, with results varying by type of social media platform. Overall, Facebook was the most common social media resource for information on vaccination.

The film industry is one of the biggest contributors to the entertainment industry and also it is characterized with its unpredictability in success and Failure. Film Industry has always amused everyone with its unpredictable success and Failure.  This research looks into the inner details of watching a movie by splitting the research into three main components. First section is exploring the variables that influence the frequency of movie watch; second, developing a model to predict the success or failure. Finally, social network sentiment analysis is carried out through data mining to capture the audience sentiment and its impact on movie’s success and failure. The research tries to look at the success or failure of a movie on a more holistic manner than trying to grade the performance of a movie over a few variables based on the previous research works on movie success prediction.

These days most people use social media sites like Facebook, Twitter, etc. to review, buying and complain about products or services. According to the previous, most companies changed from traditional CRM to SCRM to be able to retain the current Customers and also can compete with the others and get new Customers. Starting from the importance of Customer reviews about products or services for companies, we started working on this paper. Sentiment analysis model was used to get Customers opinions about product or service then manual analysis has been done on negative and positive reviews. The result of this research is beneficial reports for business decision makers to enhance SCRM.

Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, all the traditional news channels are using social media to catch the attention of users. Each news channel has its own strategy to attract more users. In this paper, we analyze how the news channels use sentiment to garner users' attention in social media. We compare the sentiment of news posts generated by television, radio and print media, to show the di erences in the news covered by these channels. We also analyze users' reactions and sentiment of users' opinions on news posts with di erent sentiments. We do our analysis on the dataset extracted from the Facebook Pages of ve popular news channels. Our dataset contains 0.15 million news posts and 1.13 billion users reactions. Our result shows that sentiment of the user opinion strongly correlates with the sentiment of news posts and the type of information source. Our study also illustrates the di erences between the social media news channels of di erent types of news sources.