Relevant bibliographies by topics / Twitter sentiment polarity classification

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Academic literature on the topic 'Twitter sentiment polarity classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Twitter sentiment polarity classification"

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Risnantoyo, Ricky, Arifin Nugroho, and Kresna Mandara. "Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 86–96. http://dx.doi.org/10.31289/jite.v4i1.3798.

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Corona virus outbreaks that occur in almost all countries in the world have an impact not only in the health sector, but also in other sectors such as tourism, finance, transportation, etc. This raises a variety of sentiments from the public with the emergence of corona virus as a trending topic on Twitter social media. Twitter was chosen by the public because it can disseminate information in real time and can see market reactions quickly. This research uses "tweet" data or public tweet related to "Corona Virus" to see how the sentiment polarity arises. Text mining techniques and three machine learning classification algorithms are used, including Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) to build a tweet classification model of sentiments whether they have positive, negative, or neutral polarity. The highest test results are generated by the Support Vector Machine (SVM) algorithm with an accuracy value of 76.21%, a precision value of 78.04%, and a recall value of 71.42%.Keywords: Machine Learning, Corona Virus, Twitter, Sentiment Analysis.
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Abu Bakar, Normi Sham Awang, Ros Aziehan Rahmat, and Umar Faruq Othman. "Polarity Classification Tool for Sentiment Analysis in Malay Language." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (December 1, 2019): 259. http://dx.doi.org/10.11591/ijai.v8.i3.pp259-263.

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<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>
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Al-Kabi, Mohammed N., Heider A. Wahsheh, and Izzat M. Alsmadi. "Polarity Classification of Arabic Sentiments." International Journal of Information Technology and Web Engineering 11, no. 3 (July 2016): 32–49. http://dx.doi.org/10.4018/ijitwe.2016070103.

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Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.
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Montejo-Ráez, Arturo, Eugenio Martínez-Cámara, M. Teresa Martín-Valdivia, and L. Alfonso Ureña-López. "Ranked WordNet graph for Sentiment Polarity Classification in Twitter." Computer Speech & Language 28, no. 1 (January 2014): 93–107. http://dx.doi.org/10.1016/j.csl.2013.04.001.

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Mahajan, Prerna, and Anamika Rana. "Sentiment Classification-How to Quantify Public Emotions Using Twitter." International Journal of Sociotechnology and Knowledge Development 10, no. 1 (January 2018): 57–71. http://dx.doi.org/10.4018/ijskd.2018010104.

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This article describes how with the tremendous popularity in the usage of social media has led to the explosive growth in unstructured data available on various social networking sites. Sentiment analysis of textual data collected from such platforms has become an important research area. In this article, the sentiment classification approach which employs an emotion detection technique is presented. To identify the emotions this paper uses the NRC lexicon based approach for identifying polarity of emotions. A score is computed to quantify emotions obtained from NRC lexicon approach. The method proposed has been tested on twitter datasets of government policies and reforms, more about current NDA government initiatives in India. The polarity components apply and classify the tweets into eight predefined emotions. This article performs both quantitative and sentiment analysis processes with the objective of analyzing the opinion conveyed to each social content, assign a category (+ve, -ve & neutral) or numbered sentiment score. The assigned scores have been classified using six different machine classification algorithms. Good classification results are achieved with the data.
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Lohar, Pintu, Haithem Afli, and Andy Way. "Maintaining Sentiment Polarity in Translation of User-Generated Content." Prague Bulletin of Mathematical Linguistics 108, no. 1 (June 1, 2017): 73–84. http://dx.doi.org/10.1515/pralin-2017-0010.

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Abstract The advent of social media has shaken the very foundations of how we share information, with Twitter, Facebook, and Linkedin among many well-known social networking platforms that facilitate information generation and distribution. However, the maximum 140-character restriction in Twitter encourages users to (sometimes deliberately) write somewhat informally in most cases. As a result, machine translation (MT) of user-generated content (UGC) becomes much more difficult for such noisy texts. In addition to translation quality being affected, this phenomenon may also negatively impact sentiment preservation in the translation process. That is, a sentence with positive sentiment in the source language may be translated into a sentence with negative or neutral sentiment in the target language. In this paper, we analyse both sentiment preservation and MT quality per se in the context of UGC, focusing especially on whether sentiment classification helps improve sentiment preservation in MT of UGC. We build four different experimental setups for tweet translation (i) using a single MT model trained on the whole Twitter parallel corpus, (ii) using multiple MT models based on sentiment classification, (iii) using MT models including additional out-of-domain data, and (iv) adding MT models based on the phrase-table fill-up method to accompany the sentiment translation models with an aim of improving MT quality and at the same time maintaining sentiment polarity preservation. Our empirical evaluation shows that despite a slight deterioration in MT quality, our system significantly outperforms the Baseline MT system (without using sentiment classification) in terms of sentiment preservation. We also demonstrate that using an MT engine that conveys a sentiment different from that of the UGC can even worsen both the translation quality and sentiment preservation.
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Hiriyannaiah, Srinidhi, G. M. Siddesh, and K. G. Srinivasa. "Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis." International Journal of Information Technology and Web Engineering 13, no. 3 (July 2018): 99–111. http://dx.doi.org/10.4018/ijitwe.2018070107.

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This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.
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Wagh, Bhagyashri, J. V. Shinde, and P. A. Kale. "A Twitter Sentiment Analysis Using NLTK and Machine Learning Techniques." International Journal of Emerging Research in Management and Technology 6, no. 12 (June 11, 2018): 37. http://dx.doi.org/10.23956/ijermt.v6i12.32.

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In today’s world, Social Networking website like Twitter, Facebook , Tumbler, etc. plays a very significant role. Twitter is a micro-blogging platform which provides a tremendous amount of data which can be used for various application of sentiment Analysis like predictions, review, elections, marketing, etc Sentiment Analysis is a process of extracting information from large amount of data, and classifies them into different classes called sentiments. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provide graphical demonstration for representing various results or trends and it also provide sample data to train and test various classifier respectively. Sentiment classification aims to automatically predict sentiment polarity of users publishing sentiment data. Although traditional classification algorithm can be used to train sentiment classifiers from manually labelled text data, the labelling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the difference between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in target domain but have some labelled data in a different domain, regarded as source domain.
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Shofiya, Carol, and Samina Abidi. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data." International Journal of Environmental Research and Public Health 18, no. 11 (June 3, 2021): 5993. http://dx.doi.org/10.3390/ijerph18115993.

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Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. Methods: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. Results: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. Conclusion: Results showed that an increase in training data increased the performance of the algorithm.
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Xing, Yongping, Chuangbai Xiao, Yifei Wu, and Ziming Ding. "A Convolutional Neural Network for Aspect-Level Sentiment Classification." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (May 15, 2019): 1959046. http://dx.doi.org/10.1142/s0218001419590468.

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Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural networks (CNNs); however, no literature on aspect-level sentiment classification that uses CNNs is available. In the present study, we develop a CNN model for handling aspect-level sentiment classification. In our model, attention-based input layers are incorporated into CNN to introduce aspect information. In our experiment, in which a benchmark dataset from Twitter is compared with other models, incorporating aspect information into CNN improves aspect-level sentiment classification performance without using syntactic parser or other language features.
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Dissertations / Theses on the topic "Twitter sentiment polarity classification"

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Di, Gennaro Pierluigi. "Due approcci alla sentiment polarity classification di tweet per la lingua italiana." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13270/.

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Questo lavoro di tesi si pone l'obiettivo di fornire un'ampia panoramica sull'attuale stato dell'arte della ricerca sulla sentiment analysis mostrando le metodologie, le tecniche e le applicazioni realizzate negli ultimi anni e di presentare le implementazioni concrete (ed i risultati ottenuti) di due diversi sistemi per la sentiment polarity classification di tweet per la lingua italiana. Il primo sistema (FICLIT+CS@Unibo System) utilizza un approccio basato sull'orientamento semantico tramite la realizzazione e l'utilizzo di un lessico annotato e la propagazione della polarità lungo alberi sintattici mentre il secondo utilizza algoritmi stocastico/statistici di machine learning per la creazione di un modello generalizzato per la classificazione del sentimento a partire da un training set annotato.
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Davrieux, Sebastian. "Studio e realizzazione di un sistema per la Sentiment Analysis basato su reti neurali ?deep?" Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Questo lavoro di tesi ha portato alla realizzazione di un sistema di polarity classification per Twitter in lingua italiana. Dato un insieme di keyword, l'obiettivo posto era quello di effettuare la ricerca ed il recupero di tweet inerenti, analizzare i risultati definendo la polarità di ogni tweet e mostrarli graficamente all'utente; ponendo particolare attenzione alla qualità dell'analisi dei tweet ed affidando il recupero e la visualizzazione grafica a sistemi esistenti. Gli studi e gli approfondimenti effettuati hanno portato alla realizzazione di un sistema di classificazione supervisionato. Il primo passo del sistema implementato consiste in un preprocessing che sfrutta le caratteristiche intrinsiche di Twitter: emoticons, emoji, hashtag, ecc. Una volta terminato il preprocessing, i tweet sono stati rappresentati vettorialmente utilizzando il metodo Paragraph Vector, nello specifico l'implementazione Doc2Vec presente nella libreria Gensim. La classificazione avviene utilizzando due Convolutional Neural Network (CNN), la prima determina se un tweet è positivo o no e la seconda agisce nello stesso modo, ma determinando se è negativo o meno. In questo modo i tweet, mediante la combinazione dei risultati di entrambi i classificatori, vengono divisi in quattro categorie: positivo, negativo, neutro e misto La valutazione del sistema è stata effettuata utilizzando i tweet di addestramento e test, forniti nella campagna di valutazione EVALITA 2016. L'idea implementata è innovativa, dato che non è mai stato presentato ad EVALITA un sistema che unisse il risultato di un modello Doc2Vec con un classificatore CNN. Il modello implementato si sarebbe classificato in seconda posizione, dimostrando le sue ottime prestazioni.
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Palm, Niklas. "Sentiment classification of Swedish Twitter data." Thesis, Uppsala universitet, Avdelningen för datalogi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388420.

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Sentiment analysis is a field within the area of natural language processing that studies the sentiment of human written text. Within sentiment analysis, sentiment classification is a research area that has been of growing interest since the advent of digital social-media platforms, concerned with the classification of the subjective information in text data. Many studies have been conducted on sentiment classification, producing numerous of openly available tools and resources that further advance research, though almost exclusively for the English language. There are very few openly available Swedish resources that aid research, and sentiment classification research in non-English languages most often use English resources one way or another. The lack of non-English resources impedes research in other languages and there is very little research on sentiment classification using Swedish resources. This thesis addresses the lack of knowledge in this area by designing and implementing a sentiment classifier using Swedish resources, in order to evaluate how methods and best practices commonly used in English research transfer to Swedish. The results in this thesis indicate that Swedish resources can be used in the construction of internationally competitive sentiment classifiers and that methods commonly used in English research for pre- processing text data may not be optimal for the Swedish language.
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Selmer, Oyvind, and Mikael Brevik. "Classification and Visualisation of Twitter Sentiment Data." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22967.

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The social micro-blog site Twitter grows in user base each day and has become an attractive platform for companies, politicians, marketeers, and others wishing to share information and/or opinions. With a growing user market for Twitter, more and more systems and research are released for taking advantage of its informal nature and doing opinion mining and sentiment analysis. This master thesis describes a system for doing Sentiment Analysis on Twitter data and experiments with grid searches on various combinations of machine learning algorithms, features and preprocessing methods to achieve so. The classification system is fairly domain independent and performs better than baseline. This system is designed to be fast enough to classify big amounts of data and tweets in a stream, and provides an application program interface (API) to easily transfer data to applications or end users. Three visualisation applications are implemented, showing how to use the API and providing examples of how sentiment data can be used.The main contributions are: C1: A literary study of the state-of-the-art for Twitter Sentiment Analysis.C2: The implementation of a general system architecture for doing Twitter Sentiment Analysis. C3: A comparison of different machine learning algorithms for the task of identifying sentiments in short messages in a fairly semi-independent domain.C4: Implementations of a set of visualisation applications, showing how to use data from the generic system and providing examples of how to present sentiment analysis data.
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Grönlund, Lucas. "Transfer learning in Swedish - Twitter sentiment classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252536.

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Language models can be applied to a diverse set of tasks with great results, but training a language model can unfortunately be a costly task, both in time and money. By transferring knowledge from one domain to another, the costly training only has to be performed once, thus opening the door for more applications. Most current research is carried out with English as the language of choice, thus limiting the amount of available already trained language models in other languages. This thesis explores how the amount of data available for training a language model effects the performance on a Twitter sentiment classification task, and was carried out using Swedish as the language of choice. The Swedish Wikipedia was used as a source for pre-training the language models which were then transferred over to a domain consisting of Swedish tweets. Several models were trained using different amounts of data from these two domains in order to compare the performance of these models. The results of the model evaluation shows that transferring knowledge from the Swedish Wikipedia to tweets yield little to no improvements, while unsupervised fine-tuning on tweets give raise to large improvements in performance.
Språkmodeller kan appliceras på en mängd olika uppgifter med bra resultat, men att träna en språkmodell kan dessvärre vara kostsamt både tids- och pengamässigt. Genom att överföra information från en domän till en annan behöver denna kostsamma träningsprocess bara genomföras en gång, och ger således lättare tillgång till dessa modeller. Dagens forskning genomförs främst med engelska som språk vilket således begränsar mängden av färdigtränade modeller på andra språk. Denna rapport utforskar hur mängden data tillgänglig för träning av språkmodeller påverkar resultatet i ett problem gällande attitydanalys av tweets, och utfördes med svenska som språk. Svenska Wikipedia användes för att först träna språkmodellerna som sedan överfördes till en domän bestående av tweets på svenska. Ett flertal språkmodeller tränades med olika mängd data från dessa två domäner för att sedan kunna jämföra deras prestanda. Resultaten visar att överföring av kunskap från Wikipedia till tweets knappt gav upphov till någon förbättring, medan oövervakad träning på tweets förbättrade resultaten markant.
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Hallsmar, Fredrik, and Jonas Palm. "Multi-class Sentiment Classification on Twitter using an Emoji Training Heuristic." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186369.

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Sentiment analysis on social media is an important part of today's need for information gathering. Different machine learning techniques have been used in recent years, and usage of an emoticon heuristic to automatically annotate training sets has been a popular approach. As emojis are becoming more popular to use in text-based communication this thesis investigates the feasibility of an emoji training heuristic for multi-class sentiment analysis using a Multinomial Naive Bayes Classifier. Training sets consisting of 4000 to 400 000 tweets were used to train the classifier using various configurations of N-grams. The results show that an emoji heuristic performs well compared to emoticon- or hashtag-based heuristics. However, classifier confusion is highly dependent on class selection and emoji representations when multi-class sentiment analysis is performed.
Sentimentanalys är ett problem av stor vikt på sociala medier. Ett flertal olika maskininlärningstekniker har använts på senare år och att använda en träningsmängd som är automatiskt annoterad med hälp av en heuristik baserad på så kallade emoticons har varit ett populärt angreppssätt. Användningen av så kallade emojis i textbaserad kommunikation har ökat på sistone. I linje med denna utveckling så ämnar studien att undersöka om det är hållbart med användning av en heuristik baserad på emojis för flerklassig sentimentanalys. Detta undersöks med hjälp av en Multinomial Naive Bayes-klassificerare som tränas med mängder av storlek 4000 till 400 000 (stycken tweets) och olika variationer av N-gram. Resultatet visar att en emojibaserad heuristik fungerar bra jämfört med en som är baserad på hashtags eller emoticons. Dock så har val av klasser och emojirepresentationer en stor påverkan på förvirringen hos klassificeraren.
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Longton, Adam. "An empirical analysis of lexical polarity and contextual valence shifters for opinion classification." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4180.

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This work is concerned with the automatic understanding of evaluative text. We investigate sentence level opinion polarity prediction by assigning lexical polarities and deriving sentence polarity from these with the use of contextual valence shifters. A methodology for iterative failure analysis is developed and used to refine our lexicon and identify new contextual shifters. Algorithms are presented that employ these new shifters to improve sentence polarity prediction accuracy beyond that of a state-of-the-art existing algorithm in the domain of consumer product reviews. We then apply the best configuration of our algorithm to the domain of movie reviews.
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David, Jäderberg. "Sentiment and topic classification of messages on Twitter : and using the results to interact with Twitter users." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-294364.

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We classify messages posted to social media network Twitter based on the sentiment and topic of the messages. We use the results of the classification to sometimes generate responses that are sent to the original user and their network on Twitter using natural language processing. A network of users who post science related content is used as the sources of data. The classifications of the dataset show worse results than others have achieved for sentiment analysis of content on Twitter, possibly due to the data sets that were used.
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Nepal, Srijan. "Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342544962.

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Araujo, Gabriela Denise. "Análise de sentimento de mensagens do Twitter em português brasileiro relacionadas a temas de saúde." Universidade Federal de São Paulo (UNIFESP), 2014. http://repositorio.unifesp.br/handle/11600/41280.

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Objetivo: Construir um método de classificação de sentimento, aqui denominado Sentiment Descriptor Indexing (SDI) ou Indexador de Descritores Sentimentais, para ser aplicado em mensagens do Twitter em português brasileiro relacionadas a temas de saúde possibilitando oferecer uma análise de sentimento com caracterização de aspectos da popularidade e repercussão dos temas. Métodos: A primeira etapa considerou a construção do algoritmo SDI que se baseia na coocorrência de termos do Twitter com descritores do vocabulário ANEW-BR. Emoticons e tratamento de negação foram incorporados no SDI. Na segunda etapa foi realizada uma avaliação do desempenho do algoritmo SDI para mensagens sobre o tema “câncer” de um pe-ríodo de três semanas. As mensagens foram classificadas por voluntários como sa-úde ou não saúde, e positiva, negativa ou neutra e em paralelo pelo SDI. As classifi-cações foram pareadas gerando uma avaliação de desempenho. Também foram geradas análise de sentimento e nuvem de termos. Na terceira etapa foi realizado um experimento de análise de sentimento para os temas “câncer” e “diabetes” em um período de seis meses, com análises de repercussão e popularidade. Resulta-dos: As classificações humana e SDI concordaram na classificação majoritária posi-tiva. Os valores de precisão e revocação resultaram 0,68 e 0,67 respectivamente, gerando melhor desempenho com f0,5-measure 0,68. No experimento coletou-se um total de 25.230 mensagens sobre o tema "câncer" com classificação de sentimento positiva (71%). Pela nuvem de palavras foi possível observar que celebridades, insti-tutos, hospitais, campanhas de saúde e tipos de câncer são assuntos populares so-bre o tema. Para o tema "diabetes" 3.328 mensagens foram coletadas com classifi-cação de sentimento positiva (78%). Para este tema as palavras mais frequentes, indicadas na nuvem de palavras, estavam relacionadas a alimentos e doenças como obesidade e hipertensão. Conclusão: Os resultados obtidos na etapa de avaliação do classificador SDI mostrou que o SDI teve um bom desempenho na tarefa de clas-sificar mensagens do Twitter sobre saúde comparada a classificação realizada por humanos. Entretanto, o tema escolhido retornou mensagens difíceis de serem rotu-ladas até mesmo pelos humanos, gerando discordâncias nas classificações. As con-tribuições deste trabalho visam suprir a falta de métodos de análise de sentimentos para a língua portuguesa brasileira bem como incentivar sua aplicação na melhoria de outras atividades em processamento de linguagem natural.
Objective: Build a sentiment classification method, named Sentiment Descriptor In-dexing (SDI), to be applied in Twitter’s messages in brazilian portuguese related to health topics, providing sentiment analysis with characterization of aspects of the popularity and impact of issues. Methods: The first step regarded the SDI algorithm construction that it is based on the cooccurence of Twitter's terms with descriptors of ANEW-BR vocabulary. Emoticons and deny treatment were embedded in the SDI. In the second step, an evaluation was performed in the algorithm SDI for messages related the topic "cancer" collected in a period of three weeks. The messages were classified by volunteers in topic about health or not health, and positive, negative or neutral and in parallel by the SDI. The ratings were paired generating a performance evaluation, sentiment analysis and cloud of terms. In the third step an experiment of sentiment analysis was performed for the topics "cancer" and "diabetes" in a period of six months, with analysis of impact and popularity. Results: The human and SDI classifications agreed in positive majority classification. The values of precision and recall resulted 0.68 and 0.67 respectively, the best performance was in f0,5-measure 0,68. In experiment, it was collected a total of 25,230 messages on "cancer" and the sentiment classification of these messages was positive (71%). Through the cloud of words was possible to observe that celebrities, institutes, hospitals, health campaigns and types of cancers are popular subjects on the topic. For the topic "diabetes", 3,328 messages were collected and the sentimental classification was positive (78%). For this topic the most frequent words, given the cloud of words were related to food and diseases such as obesity and hypertension. Conclusions: The results obtained in the evaluation step showed that the SDI had a good performance in the task of classifying Twitter’s messages about health topics compared the classification performed by humans. However, the topic chosen brought messages difficult to be labeled even by humans, causing disagreements in the classifications among them. The contributions of this work aims to meet the lack of sentiment analysis methods for the brazilian portuguese language and encourage its application in improving oth-er activities in natural language processing.
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Book chapters on the topic "Twitter sentiment polarity classification"

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Bi, Yaxin. "Evidential Fusion for Sentiment Polarity Classification." In Belief Functions: Theory and Applications, 365–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11191-9_40.

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Van Canneyt, Steven, Nathan Claeys, and Bart Dhoedt. "Topic-Dependent Sentiment Classification on Twitter." In Lecture Notes in Computer Science, 441–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_48.

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Dritsas, Elias, Gerasimos Vonitsanos, Ioannis E. Livieris, Andreas Kanavos, Aristidis Ilias, Christos Makris, and Athanasios Tsakalidis. "Pre-processing Framework for Twitter Sentiment Classification." In IFIP Advances in Information and Communication Technology, 138–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19909-8_12.

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Supriya, B. N., Vish Kallimani, S. Prakash, and C. B. Akki. "Twitter Sentiment Analysis Using Binary Classification Technique." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 391–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46909-6_36.

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Trindade, Luis, Hui Wang, William Blackburn, and Niall Rooney. "Factored Semantic Sequence Kernel for Sentiment Polarity Classification." In Statistical Language and Speech Processing, 284–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39593-2_25.

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Trindade, Luis A., Hui Wang, William Blackburn, and Niall Rooney. "An Enhanced Semantic Tree Kernel for Sentiment Polarity Classification." In Computational Linguistics and Intelligent Text Processing, 50–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37256-8_5.

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Tan, Luke Kien-Weng, Jin-Cheon Na, Yin-Leng Theng, and Kuiyu Chang. "Sentence-Level Sentiment Polarity Classification Using a Linguistic Approach." In Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation, 77–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24826-9_13.

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Yanagimoto, Hidekazu, Mika Shimada, and Akane Yoshimura. "Word Classification for Sentiment Polarity Estimation Using Neural Network." In Human Interface and the Management of Information. Information and Interaction Design, 669–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39209-2_75.

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Zhang, Yaowen, Xiaojun Xiang, Cunyan Yin, and Lin Shang. "Parallel Sentiment Polarity Classification Method with Substring Feature Reduction." In Lecture Notes in Computer Science, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40319-4_11.

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Tsakalidis, Adam, Symeon Papadopoulos, and Ioannis Kompatsiaris. "An Ensemble Model for Cross-Domain Polarity Classification on Twitter." In Web Information Systems Engineering – WISE 2014, 168–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11746-1_12.

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Conference papers on the topic "Twitter sentiment polarity classification"

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Lima, Raul de Araújo, and Paulo T. Guerra. "An Analysis of the Sentiment Classification of Short Messages Using Word2Vec." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4436.

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Abstract:
Sentiment analisys and the polarity classification of texts constitute one of the main tools currently used by companies and organizations for the most varied purposes. This work presents an analysis of the use of word embeddings, built through Word2Vec, in the process of features extraction for polarity classification of short messages written in English. The texts used were extracted from Twitter and the results obtained show that, in spite of the possible need to use larger textual bases to obtain better vectors, Word2Vec is a promising tool for the features extraction of textual data, contributing to obtain good classification results.
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Mao, Qianren, Jianxin Li, Senzhang Wang, Yuanning Zhang, Hao Peng, Min He, and Lihong Wang. "Aspect-Based Sentiment Classification with Attentive Neural Turing Machines." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/714.

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Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given opinion target in a sentence. The sentiment polarity of the target is not only highly determined by sentiment semantic context but also correlated with the concerned opinion target. Existing works cannot effectively capture and store the inter-dependence between the opinion target and its context. To solve this issue, we propose a novel model of Attentive Neural Turing Machines (ANTM). Via interactive read-write operations between an external memory storage and a recurrent controller, ANTM can learn the dependable correlation of the opinion target to context and concentrate on crucial sentiment information. Specifically, ANTM separates the information of storage and computation, which extends the capabilities of the controller to learn and store sequential features. The read and write operations enable ANTM to adaptively keep track of the interactive attention history between memory content and controller state. Moreover, we append target entity embeddings into both input and output of the controller in order to augment the integration of target information. We evaluate our model on SemEval2014 dataset which contains reviews of Laptop and Restaurant domains and Twitter review dataset. Experimental results verify that our model achieves state-of-the-art performance on aspect-based sentiment classification.
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Ai, Xinzhi, Xiaoge Li, Feixiong Hu, Shuting Zhi, and Likun Hu. "Multi-Layer Attention Approach for Aspect based Sentiment Analysis." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101410.

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Based on the aspect-level sentiment analysis is typical of fine-grained emotional classification that assigns sentiment polarity for each of the aspects in a review. For better handle the emotion classification task, this paper put forward a new model which apply Long Short-Term Memory network combine multiple attention with aspect context. Where multiple attention mechanism (i.e., location attention, content attention and class attention) refers to takes the factors of context location, content semantics and class balancing into consideration. Therefore, the proposed model can adaptively integrate location and semantic information between the aspect targets and their contexts into sentimental features, and overcome the model data variance introduced by the imbalanced training dataset. In addition, the aspect context is encoded on both sides of the aspect target, so as to enhance the ability of the model to capture semantic information. The Multi-Attention mechanism (MATT) and Aspect Context (AC) allow our model to perform better when facing reviews with more complicated structures. The result of this experiment indicate that the accuracy of the new model is up to 80.6% and 75.1% for two datasets in SemEval-2014 Task 4 respectively, While the accuracy of the data set on twitter 71.1%, and 81.6% for the Chinese automotive-domain dataset. Compared with some previous models for sentiment analysis, our model shows a higher accuracy.
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Kaljahi, Rasoul, and Jennifer Foster. "Sentiment Expression Boundaries in Sentiment Polarity Classification." In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-6222.

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Dinsoreanu, Mihaela, and Andrei Bacu. "Unsupervised Twitter Sentiment Classification." In International Conference on Knowledge Management and Information Sharing. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005079002200227.

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Li, Shoushan, Zhongqing Wang, Sophia Yat Mei Lee, and Chu-Ren Huang. "Sentiment Classification with Polarity Shifting Detection." In 2013 International Conference on Asian Language Processing (IALP). IEEE, 2013. http://dx.doi.org/10.1109/ialp.2013.44.

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Ansari, Daniel. "Sentiment Polarity Classification Using Structural Features." In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2015. http://dx.doi.org/10.1109/icdmw.2015.57.

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Colhon, Mihaela, Madalina Cerban, Alex Becheru, and Mirela Teodorescu. "Polarity shifting for Romanian sentiment classification." In 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2016. http://dx.doi.org/10.1109/inista.2016.7571834.

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Lek, Hsiang Hui, and Danny C. C. Poo. "Aspect-Based Twitter Sentiment Classification." In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2013. http://dx.doi.org/10.1109/ictai.2013.62.

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Barnaghi, Peiman, Parsa Ghaffari, and John G. Breslin. "Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment." In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2016. http://dx.doi.org/10.1109/bigdataservice.2016.36.

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