Relevant bibliographies by topics / Sentiment Analysis Opinion Mining Text Mining Twitter

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Academic literature on the topic 'Sentiment Analysis Opinion Mining Text Mining Twitter'

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

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Journal articles on the topic "Sentiment Analysis Opinion Mining Text Mining Twitter"

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Reyhana, Zakya, Kartika Fithriasari, Moh Atok, and Nur Iriawan. "Linking Twitter Sentiment Knowledge with Infrastructure Development." MATEMATIKA 34, no. 3 (2018): 91–102. http://dx.doi.org/10.11113/matematika.v34.n3.1142.

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Sentiment analysis is related to the automatic extraction of positive or negative opinions from the text. It is a special text mining application. It is important to classify implicit contents from citizen’s tweet using sentiment analysis. This research aimed to find out the opinion of infrastructure that sustained urban development in Surabaya, Indonesia’s second largest city. The procedures of text mining analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT), username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified into positive and negative comments. Classification methods used in this research were support vector machine (SVM) and neural network (NN). The result of this research showed that NN classification method was better than SVM.
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Purohit, Amit. "Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 233–39. http://dx.doi.org/10.22214/ijraset.2021.36202.

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Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.
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Chinedum, Amaechi, and Okeke Ogochukwu C. "A Review on Opinion Mining: Approaches, Practices and Application." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 3 (2021): 01–06. http://dx.doi.org/10.17762/ijritcc.v9i3.5456.

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Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms.
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Bahrawi, Nfn. "Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter." Jurnal Penelitian Pos dan Informatika 9, no. 1 (2019): 53. http://dx.doi.org/10.17933/jppi.2019.090105.

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<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>
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Bourequat, Wasim, and Hassan Mourad. "Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine." International Journal of Advances in Data and Information Systems 2, no. 1 (2021): 36–44. http://dx.doi.org/10.25008/ijadis.v2i1.1216.

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Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.
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Jain, Kirti. "Sentiment Analysis on Twitter Airline Data." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3767–70. http://dx.doi.org/10.22214/ijraset.2021.35807.

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Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.
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Ritika Siril Paul, Yazala, and Dilipkumar A. Borikar. "An Approach To Twitter Sentiment Analysis Over Hadoop." International Journal of Engineering & Technology 7, no. 4.5 (2018): 374. http://dx.doi.org/10.14419/ijet.v7i4.5.20110.

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Sentiment analysis is the process of identifying people’s attitude and emotional state from the language they use via any social websites or other sources. The main aim is to identify a set of potential features in the review and extract the opinion expressions of those features by making full use of their associations. The Twitter has now become a routine for the people around the world to post thousands of reactions and opinions on every topic, every second of every single day. It’s like one big psychological database that’s constantly being updated and which can be used to analyze the sentiments of the people. Hadoop is one of the best options available for twitter data sentiment analysis and which also works for the distributed big data, streaming data, text data etc. This paper provides an efficient mechanism to perform sentiment analysis/ opinion mining on Twitter data over Hortonworks Data platform, which provides Hadoop on Windows, with the assistance of Apache Flume, Apache HDFS and Apache Hive.
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Steven, Cristian, and Wella Wella. "The Right Sentiment Analysis Method of Indonesian Tourism in Social Media Twitter." IJNMT (International Journal of New Media Technology) 7, no. 2 (2020): 102–10. http://dx.doi.org/10.31937/ijnmt.v7i2.1732.

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The growth of social media is changing the way humans communicate with each other, many people use social media such as Twitter to express opinions, experiences and other things that concern them, where things like this are often referred to as sentiments. The concept of social media is now the focus of business people to find out people's sentiments about a product or place that will become a business. Sentiment Analysis or often also called opinion mining is a computational study of people's opinions, appraisal, and emotions through entities, events and attributes owned. Sentiment analysis itself has recently become a popular topic for research because sentiment analysis can be applied in many industrial sectors, one of which is the tourism industry in Indonesia. To be able to do a sentiment analysis requires mastery of several techniques such as techniques for doing text mining, machine learning and natural language processing (NLP) to be able to process large and unstructured data coming from social media. Some methods that are often used include Naive Bayes, Neural Networks, K-Nearest Neighbor, Support Vector Machines, and Decision Tree. Because of this, this research will compare these four algorithms so that an algorithm can be used to analyze people's sentiments towards the city of Bali.
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Bahrawi, Nfn. "Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based." Journal of Information Technology and Its Utilization 2, no. 2 (2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.

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Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings.
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Karami, Amir, London S. Bennett, and Xiaoyun He. "Mining Public Opinion about Economic Issues." International Journal of Strategic Decision Sciences 9, no. 1 (2018): 18–28. http://dx.doi.org/10.4018/ijsds.2018010102.

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Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.
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Dissertations / Theses on the topic "Sentiment Analysis Opinion Mining Text Mining Twitter"

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Di, Paolo Denis. "Metodi di previsione di indici di borsa con tecniche di text & opinion mining applicate a twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6433/.

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Il problema relativo alla predizione, la ricerca di pattern predittivi all‘interno dei dati, è stato studiato ampiamente. Molte metodologie robuste ed efficienti sono state sviluppate, procedimenti che si basano sull‘analisi di informazioni numeriche strutturate. Quella testuale, d‘altro canto, è una tipologia di informazione fortemente destrutturata. Quindi, una immediata conclusione, porterebbe a pensare che per l‘analisi predittiva su dati testuali sia necessario sviluppare metodi completamente diversi da quelli ben noti dalle tecniche di data mining. Un problema di predizione può essere risolto utilizzando invece gli stessi metodi : dati testuali e documenti possono essere trasformati in valori numerici, considerando per esempio l‘assenza o la presenza di termini, rendendo di fatto possibile una utilizzazione efficiente delle tecniche già sviluppate. Il text mining abilita la congiunzione di concetti da campi di applicazione estremamente eterogenei. Con l‘immensa quantità di dati testuali presenti, basti pensare, sul World Wide Web, ed in continua crescita a causa dell‘utilizzo pervasivo di smartphones e computers, i campi di applicazione delle analisi di tipo testuale divengono innumerevoli. L‘avvento e la diffusione dei social networks e della pratica di micro blogging abilita le persone alla condivisione di opinioni e stati d‘animo, creando un corpus testuale di dimensioni incalcolabili aggiornato giornalmente. Le nuove tecniche di Sentiment Analysis, o Opinion Mining, si occupano di analizzare lo stato emotivo o la tipologia di opinione espressa all‘interno di un documento testuale. Esse sono discipline attraverso le quali, per esempio, estrarre indicatori dello stato d‘animo di un individuo, oppure di un insieme di individui, creando una rappresentazione dello stato emotivo sociale. L‘andamento dello stato emotivo sociale può condizionare macroscopicamente l‘evolvere di eventi globali? Studi in campo di Economia e Finanza Comportamentale assicurano un legame fra stato emotivo, capacità nel prendere decisioni ed indicatori economici. Grazie alle tecniche disponibili ed alla mole di dati testuali continuamente aggiornati riguardanti lo stato d‘animo di milioni di individui diviene possibile analizzare tali correlazioni. In questo studio viene costruito un sistema per la previsione delle variazioni di indici di borsa, basandosi su dati testuali estratti dalla piattaforma di microblogging Twitter, sotto forma di tweets pubblici; tale sistema include tecniche di miglioramento della previsione basate sullo studio di similarità dei testi, categorizzandone il contributo effettivo alla previsione.
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Pruccoli, Andrea. "Strumenti di monitoraggio di dati web non strutturati." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10465/.

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Negli ultimi anni i documenti web hanno attratto molta attenzione, poiché vengono visti come un nuovo mezzo che porta quello che sono le esperienze ed opinioni di un individuo da una parte all'altra del mondo, raggiungendo quindi persone che mai si incontreranno. Ed è proprio con la proliferazione del Web 2.0 che l’attenzione è stata incentrata sul contenuto generato dagli utenti della rete, i quali hanno a disposizione diverse piattaforme sulle quali condividere i loro pensieri, opinioni o andare a cercarne di altrui, magari per valutare l’acquisto di uno smartphone piuttosto che un altro o se valutare l’opzione di cambiare operatore telefonico, ponderando quali potrebbero essere gli svantaggi o i vantaggi che otterrebbe modificando la sia situazione attuale. Questa grande disponibilità di informazioni è molto preziosa per i singoli individui e le organizzazioni, che devono però scontrarsi con la grande difficoltà di trovare le fonti di tali opinioni, estrapolarle ed esprimerle in un formato standard. Queste operazioni risulterebbero quasi impossibili da eseguire a mano, per questo è nato il bisogno di automatizzare tali procedimenti, e la Sentiment Analysis è la risposta a questi bisogni. Sentiment analysis (o Opinion Mining, come è chiamata a volte) è uno dei tanti campi di studio computazionali che affronta il tema dell’elaborazione del linguaggio naturale orientato all'estrapolazione delle opinioni. Negli ultimi anni si è rilevato essere uno dei nuovi campi di tendenza nel settore dei social media, con una serie di applicazioni nel campo economico, politico e sociale. Questa tesi ha come obiettivo quello di fornire uno sguardo su quello che è lo stato di questo campo di studio, con presentazione di metodi e tecniche e di applicazioni di esse in alcuni studi eseguiti in questi anni.
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Hernández, Martínez Víctor Alejandro. "Identificación de la presencia de ironía en el texto generado por usuarios de Twitter utilizando técnicas de Opinion Mining y Machine Learning." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/134793.

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Ingeniero Civil Industrial<br>El siguiente trabajo tiene como objetivo general dise~nar e implementar un módulo clasificador de texto que permita identificar la presencia de ironía en el contenido generado por usuarios de Twitter, mediante el uso de herramientas asociadas a Opinion Mining y Machine Learning. La ironía es un fenómeno que forma parte del contenido generado por las personas en la Web, y representa un campo de estudio nuevo que ha atraído la atención de algunos investigadores del área de Opinion Mining debido a su complejidad y al impacto que puede tener en el desempeño de las aplicaciones de Análisis de Sentimientos actuales. Este trabajo de título se desarrolla dentro del marco de OpinionZoom, proyecto CORFO código 13IDL2-23170 titulado "OpinionZoom: Plataforma de análisis de sentimientos e ironía a partir de la información textual en redes sociales para la caracterización de la demanda de productos y servicios" desarrollado en el Web Intelligence Centre del Departamento de Ingeniería Industrial de la Facultad de Ciencias Físicas y Matemáticas de la Universidad de Chile, el cual busca generar un sistema avanzado para analizar datos extraídos desde redes sociales para obtener información relevante para las empresas en relación a sus productos y servicios. La hipótesis de investigación de este trabajo dice que es posible detectar la presencia de ironía en texto en idioma Español con cierto nivel de precisión, utilizando una adaptación de la metodología propuesta por Reyes et al. (2013) en [5] la cual involucra la construcción de un corpus en función de la estructura de Twitter junto con la capacidad de las personas para detectar ironía. El modelo utilizado se compone de 11 atributos entre los cuales se rescatan características sintácticas, semánticas y emocionales o psicológicas, con el objetivo de poder describir ironía en texto. Para esto, se genera un corpus de casos irónicos y no irónicos a partir de una selección semiautomática utilizando una serie de hashtags en Twitter, para luego validar su etiquetado utilizando evaluadores humanos. Además, esto se complementa con la inclusión de textos objetivos como parte del set de casos no irónicos. Luego, utilizando este corpus, se pretende realizar el entrenamiento de un algoritmo de aprendizaje supervisado para realizar la posterior clasificación de texto. Para ésto, se implementa un módulo de extracción de atributos que transforma cada texto en un vector representativo de los atributo. Finalmente, se utilizan los vectores obtenidos para implementar un módulo clasificador de texto, el cual permite realizar una clasificación entre tipos irónicos y no irónicos de texto. Para probar su desempe~no, se realizan dos pruebas. La primera utiliza como casos no irónicos los textos objetivos y la segunda utiliza como casos no irónicos aquellos textos evaluados por personas como tales. La primera obtuvo un alto nivel de precisión, mientras que la segunda fue insuficiente. En base a los resultados se concluye que esta implementación no es una solución absoluta. Existen algunas limitaciones asociadas a la construcción del corpus, las herramientas utilizadas e incluso el modelo, sin embargo, los resultados muestran que bajo ciertos escenarios de comparación, es posible detectar ironía en texto por lo que se cumple la hipótesis. Se sugiere ampliar la investigación, mejorar la obtención del corpus, utilizar herramientas más desarrolladas y analizar aquellos elementos que el modelo no puede capturar.
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Ricci, Mattia. "Sentiment analysis su test prenatali: un caso di studio basato su Twitter e reddit." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Vivendo in un'era in cui la tecnologia è alla portata di tutti, le informazioni relative ad eventi, oggetti e persone sono divenute chiare e veloci da reperire per tutti, soprattutto per i ricercatori, i quali non sono più costretti a richiedere informazioni persona per persona, ma possono trovare ciò che cercano direttamente in rete. Per questo motivo, i sondaggi tradizionali non si presentano più come strumenti ottimali per far fronte al costante divenire della (cyber)opinione; sono costosi da progettare e implementare, non che statici per definizione. Negli ultimi 15 anni è divenuta sempre più popolare la sentiment analysis, termine che indica un approccio indirizzato classificare le opinioni contenute in un testo scritto, tramite processi informatici, al fine di estrarre informazioni soggettive, opinioni e sentimenti dalle fonti di analisi osservate. La sua notorità è andata di pari passo con la massiccia diffusione ed importanza acquistata dai social network quali Twitter, Facebook e Google+, proprio perchè questi social network sono diventati un mezzo tramite il quale gli utenti si possono esprimere, riportando i momenti positivi e negativi passati durante la giornata. Il progetto di tesi sviluppato nasce da una collaborazione con il Detroit Medical Center e la Wayne State University, in seguito a progressi sostanziali avvenuti di recente nel campo medico delle diagnosi prenatali. Vi era la necessità di compiere un'analisi automatizzata dell'umore dei pazienti ai quali vengono prescritti una serie di differenti esami prenatali. Questo studio si pone quindi l'obiettivo di permettere una visione grafica sentiment-oriented di una serie di parole chiave che verranno all’interno del documento di tesi. I dati raccolti copriranno un intervallo di tempo di 7 anni, e sarà interessante analizzare il cambiamento nell'intensità del Sentiment delle tecniche tradizionali con l'avanzare degli anni e con le nuove possibilità tecnologiche che hanno fornito le nuove alternative
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Mulazzani, Alberto. "Social media sensing: Twitter e Reddit come casi di studio e comparazione applicati ai test prenatali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Avere un figlio per molte persone può essere la gioia più grande della loro vita, ma la gravidanza è uno dei momenti più delicati della vita di una donna e come tale va controllata accuratamente in ogni suo aspetto. Non sempre questo processo è esente da rischi, e quello che è un momento di felicità si può trasformare in un momento difficile. Questo studio si prefigge l'obiettivo di permettere una visione grafica sentiment-oriented di una serie di parole chiave riferite al mondo delle diagnosi prenatali e dei test prenatali. Saranno presentati i dati ottenuti da due piattaforme di Social Networking: Reddit e Twitter nel lasso temporale che va dal 01/01/2011 al 31/03/2018 per rispondere a due domande fondamentali: Quanto è cambiato il volume di dati durante il periodo di tempo analizzato, con valore unitario di un mese e quanto è cambiato il sentiment o opinione dei dati durante il periodo di tempo analizzato, con valore unitario di un mese.
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Melloncelli, Damiano. "Sentiment analysis in Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6592/.

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Gli ultimi anni hanno visto una crescita esponenziale nell’uso dei social media (recensioni, forum, discussioni, blog e social network); le persone e le aziende utilizzano sempre più le informazioni (opinioni e preferenze) pubblicate in questi mezzi per il loro processo decisionale. Tuttavia, il monitoraggio e la ricerca di opinioni sul Web da parte di un utente o azienda risulta essere un problema molto arduo a causa della proliferazione di migliaia di siti; in più ogni sito contiene un enorme volume di testo non sempre decifrabile in maniera ottimale (pensiamo ai lunghi messaggi di forum e blog). Inoltre, è anche noto che l’analisi soggettiva delle informazioni testuali è passibile di notevoli distorsioni, ad esempio, le persone tendono a prestare maggiore attenzione e interesse alle opinioni che risultano coerenti alle proprie attitudini e preferenze. Risulta quindi necessario l’utilizzo di sistemi automatizzati di Opinion Mining, per superare pregiudizi soggettivi e limitazioni mentali, al fine di giungere ad una metodologia di Sentiment Analysis il più possibile oggettiva.
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Li, Hanzhe. "Sentiment Analysis and Opinion Mining on Twitter with GMO Keyword." Thesis, North Dakota State University, 2016. http://hdl.handle.net/10365/25787.

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Twitter are a new source of information for data mining techniques. Messages posted through Twitter provide a major information source to gauge public sentiment on topics ranging from politics to fashion trends. The purpose of this paper is to analyze the Twitter tweets to discern the opinions of users regarding Genetically Modified Organisms (GMOs). We examine the effectiveness of several classifiers, Multinomial Na?ve Bayes, Bernoulli Na?ve Bayes, Logistic Regression and Linear Support Vector Classifier (SVC) in identifying a positive, negative or neutral category on a tweet corpus. Additionally, we use three datasets in this experiment to examine which dataset has the best score. Comparing the classifiers, we discovered that GMO_NDSU has the highest score in each classifier of my experiment among three datasets, and Linear SVC had the highest consistent accuracy by using bigrams as feature extraction and Term Frequency, Chi Square as feature selection.
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Duarte, Eduardo Santos. "Sentiment analysis on twitter for the portuguese language." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11338.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>With the growth and popularity of the internet and more specifically of social networks, users can more easily share their thoughts, insights and experiences with others. Messages shared via social networks provide useful information for several applications, such as monitoring specific targets for sentiment or comparing the public sentiment on several targets, avoiding the traditional marketing research method with the use of surveys to explicitly get the public opinion. To extract information from the large amounts of messages that are shared, it is best to use an automated program to process these messages. Sentiment analysis is an automated process to determine the sentiment expressed in natural language in text. Sentiment is a broad term, but here we are focussed in opinions and emotions that are expressed in text. Nowadays, out of the existing social network websites, Twitter is considered the best one for this kind of analysis. Twitter allows users to share their opinion on several topics and entities, by means of short messages. The messages may be malformed and contain spelling errors, therefore some treatment of the text may be necessary before the analysis, such as spell checks. To know what the message is focusing on it is necessary to find these entities on the text such as people, locations, organizations, products, etc. and then analyse the rest of the text and obtain what is said about that specific entity. With the analysis of several messages, we can have a general idea on what the public thinks regarding many different entities. It is our goal to extract as much information concerning different entities from tweets in the Portuguese language. Here it is shown different techniques that may be used as well as examples and results on state-of-the-art related work. Using a semantic approach, from these messages we were able to find and extract named entities and assigning sentiment values for each found entity, producing a complete tool competitive with existing solutions. The sentiment classification and assigning to entities is based on the grammatical construction of the message. These results are then used to be viewed by the user in real time or stored to be viewed latter. This analysis provides ways to view and compare the public sentiment regarding these entities, showing the favourite brands, companies and people, as well as showing the growth of the sentiment over time.
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Montesinos, García Lucas. "Análisis de sentimientos y predicción de eventos en twitter." Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/130479.

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Ingeniero Civil Eléctrico<br>El análisis de sentimientos o sentiment analysis es el estudio por el cual se determina la opinión de las personas en Internet sobre algún tema en específico, prediciendo la polaridad de los usuarios (a favor, en contra, neutro, etc), abarcando temas que van desde productos, películas, servicios a intereses socio-culturales como elecciones, guerras, fútbol, etc. En el caso particular de esta memoria, se estudian los principales métodos usados en la literatura para realizar un análisis de sentimientos y se desarrolla un caso empleando parte de estas técnicas con sus respectivos resultados. La plataforma escogida fue Twitter, debido a su alto uso en Chile y el caso de estudio trata acerca de las elecciones presidenciales primarias realizadas en la Alianza por Chile entre los candidatos Andrés Allamand de Renovación Nacional (RN) y Pablo Longueira del partido Unión Demócrata Independiente (UDI). De esta forma, se busca predecir los resultados de las primarias, identificando la gente que está a favor de Allamand y la gente que apoya a Longueira. De igual manera, se busca identificar a los usuarios que están en contra de uno o ambos candidatos. Para predecir la opinión de los usuarios se diseñó un diccionario con palabras positivas y negativas con un puntaje asociado, de manera que al encontrar estos términos en los tweets se determina la polaridad del mensaje pudiendo ser positiva, neutra o negativa. El Algoritmo diseñado tiene un acierto cercano al 60% al ocupar las 3 categorías, mientras que si sólo se ocupa para determinar mensajes positivos y negativos la precisión llega a un 74%. Una vez catalogados los tweets se les asigna el puntaje a sus respectivos usuarios de manera de sumar estos valores a aquellas cuentas que tengan más de un tweet, para luego poder predecir el resultado de las elecciones por usuario. Finalmente, el algoritmo propuesto determina como ganador a Pablo Longueira (UDI) por sobre Andrés Allamand (RN) con un 53% de preferencia mientras que en las elecciones en urnas realizadas en Julio de 2013 en Chile el resultado fue de un 51% sobre 49% a favor de Longueira, lo cual da un error de un 2%, lo que implica que el análisis realizado fue capaz de predecir, con un cierto margen de error, lo que sucedió en las elecciones. Como trabajo futuro se plantea usar el diccionario y algoritmo diseñados para realizar un análisis de sentimientos en otro tema de interés y comprobar su efectividad para diferentes casos y plataformas.
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Balazs, Thenot Jorge-Andrés Jean-Michel. "Diseño, desarrollo e implementación de una aplicación de web opinion mining para identificar el sentimiento de usuarios de Twitter con respecto a una compañia de retail." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/137769.

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Ingeniero Civil Industrial<br>Los contenidos disponibles en la Web están creciendo a velocidades que hacen que la tarea de analizarlos sea humanamente imposible. Una de las disciplinas que hace frente a este problema es la Minería de Opiniones, también conocida como el Análisis de Sentimientos, responsable de procesar texto automáticamente, con el fin de extraer y analizar las opiniones que contiene para generar información valiosa y accionable. El objetivo principal de este trabajo es crear una aplicación de Minería de Opiniones capaz de explotar tweets en español que mencionen a la empresa de retail Falabella. En primer lugar, se investigó el impacto que las redes sociales tienen en Chile. En segundo lugar, se elaboró un estado del arte que englobara los últimos avances en Minería de Opiniones y en Procesamiento del Lenguaje Natural. En tercer lugar, se creó un Web Crawler capaz de obtener los tweets que mencionanaran a la compañía. Posteriormente se implementó varios algoritmos de Procesamiento del Lenguaje Natural para pre-procesar los tweets previamente mencionados, e incorporar los datos resultantes al proceso de extracción de opiniones. Este proceso se desarrolló como un enfoque de Minería de Opiniones no supervisado basado en lexicones, dependiente de un analizador de dependencias encargado de detectar ciertas estructuras gramaticales que permitieran identificar fenómenos linguísticos comunes, tales como la negación, intensificación, y oraciones subordinadas adversativas. La identificación de dichos fenómenos permitió mejorar la calidad de la clasificación. Finalmente se creó una página Web para mostrar los resultados que luego fueron utilizados para realizar un análisis exploratorio de la compañía. Adicionalmente, los algoritmos fueron validados con el corpus TASS, obteniendo valores-F de un 61,88% negativo y 71,88% positivo. A pesar de que el rendimiento de los algoritmos no fue tan alto como una aplicación en producción lo requeriría, se consideró lo suficientemente bueno como para realizar el análisis exploratorio. Con éste fue posible confirmar la intuición de que las cuentas corporativas suelen publicar contenido positivo, las cuentas de noticias contenido neutral, y los usuarios comunes contenido irrelevante o quejas. Además fue posible probar que los usuarios más activos frecuentemente publican contenido totalmente irrelevante. Por otra parte, se logró replicar varios resultados obtenidos por instituciones nacionales reconocidas, entre los cuales destaca el hecho que el momento más controversial del año para Falabella fue cuando se intentó llevar a cabo el Cyber Monday, período en el cual el sentimiento generalizado en Twitter alcanzó los niveles más negativos. Dicho todo esto, la aplicación desarrollada demostró ser útil al momento de utilizar una gran cantidad de datos para extraer información que podría ser potencialmente útil para la firma de retail. Finalmente, el desarrollo de la aplicación permitió crear un artículo que contuviera parte considerable del transfondo teórico en el cual ésta se basó, además de beneficiar a otros estudiantes en el desarrollo de sus memorias.
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Books on the topic "Sentiment Analysis Opinion Mining Text Mining Twitter"

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Pang, Bo. Opinion mining and sentiment analysis. Now Publishers, 2008.

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Book chapters on the topic "Sentiment Analysis Opinion Mining Text Mining Twitter"

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Sonntag, Jonathan, and Manfred Stede. "Sentiment Analysis: What’s Your Opinion?" In Text Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12655-5_9.

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Zong, Chengqing, Rui Xia, and Jiajun Zhang. "Sentiment Analysis and Opinion Mining." In Text Data Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0100-2_8.

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Aggarwal, Charu C. "Opinion Mining and Sentiment Analysis." In Machine Learning for Text. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_13.

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Liu, Bing, and Lei Zhang. "A Survey of Opinion Mining and Sentiment Analysis." In Mining Text Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3223-4_13.

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Aquino, Pâmella A., Vivian F. López, María N. Moreno, María D. Muñoz, and Sara Rodríguez. "Opinion Mining System for Twitter Sentiment Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61705-9_38.

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Delmonte, Rodolfo, and Vincenzo Pallotta. "Opinion Mining and Sentiment Analysis Need Text Understanding." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21384-7_6.

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Akkarapatty, Neethu, Anjaly Muralidharan, Nisha S. Raj, and Vinod P. "Dimensionality Reduction Techniques for Text Mining." In Collaborative Filtering Using Data Mining and Analysis. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0489-4.ch003.

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Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains.
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Andrade, Carina Sofia, and Maribel Yasmina Santos. "Sentiment Analysis with Text Mining in Contexts of Big Data." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch047.

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The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.
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Bhatia, Tarandeep Kaur. "Agile-Model-Based Sentiment Analysis From Social Media." In Big Data Management and the Internet of Things for Improved Health Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5222-2.ch002.

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To study state-of-the art associated to Twitter mining replica as well as prognostic analytic by means of Agile Software Engineering. To recognize sentiment analysis by means of agile knowledge. To obtain as well as analysis given repository for classifying sentiments into positive, negative and neutral emotions. Analysing of all the tweets obtained from the twitter keywords as positive, negative or neutral opinions and comparing all the keywords to judge which keyword is better, there is a requirement to improve from the conventional ways of sentiment analysis. This paper emphasizes on the implementation of an algorithm for automatic classification of text into positive, negative or neutral by fetching the live tweets from twitter server by using twitter API. Graphical representation of the sentiment for the purpose of comparison in the form of pie chart and bar graph. Scan the twitter and fetching the Live Tweets from Twitter server using Twitter4J Advance Java Interface and implementing the Stanford NLP Library (Natural Language Parsing) using Advance Java for classifying the tweets into positive, negative and neutral tweets.
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"Opinion Mining and Sentiment Analysis." In Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining. ACM, 2016. http://dx.doi.org/10.1145/2915031.2915050.

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Conference papers on the topic "Sentiment Analysis Opinion Mining Text Mining Twitter"

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Geetha, R., Pasupuleti Rekha, and S. Karthika. "Twitter Opinion Mining and Boosting Using Sentiment Analysis." In 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP). IEEE, 2018. http://dx.doi.org/10.1109/icccsp.2018.8452838.

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Gokulakrishnan, Balakrishnan, Pavalanathan Priyanthan, Thiruchittampalam Ragavan, Nadarajah Prasath, and AShehan Perera. "Opinion mining and sentiment analysis on a Twitter data stream." In 2012 International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2012. http://dx.doi.org/10.1109/icter.2012.6423033.

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Molla, Alemu, Yenewondim Biadgie, and Kyung-Ah Sohn. "Network-Based Visualization of Opinion Mining and Sentiment Analysis on Twitter." In 2014 International Conference on IT Convergence and Security (ICITCS). IEEE, 2014. http://dx.doi.org/10.1109/icitcs.2014.7021790.

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Zhang, Danchen, Jie Zhang, Yuqi Zhang, and Yuxin Wu. "Sentiment Analysis of China's Education Policy Online Opinion Based on Text Mining." In 2021 9th International Conference on Information and Education Technology (ICIET). IEEE, 2021. http://dx.doi.org/10.1109/iciet51873.2021.9419585.

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Bouazizi, Mondher, and Tomoaki Ohtsuki. "Opinion Mining in Twitter How to Make Use of Sarcasm to Enhance Sentiment Analysis." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2809350.

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Ramanathan, Vallikannu, and T. Meyyappan. "Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism." In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2019. http://dx.doi.org/10.1109/icbdsc.2019.8645596.

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Stepchenkova, Svetlana, and Andrei Kirilenko. "Public opinion mining on Sochi-2014 Olympics." In CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics. Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/carma2016.2016.3102.

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The requirements of evidence-based policymaking promote interest to realtime monitoring of public’s opinions on policy-relevant topics, and social media data mining allows diversification of information portfolio used by public administrators. This study discusses issues in public opinion mining with respect to extraction and analysis of information posted on Twitter about Sochi-2014 Olympic. It focuses on topics discussed on Twitter and sentiment analysis of tweets about the Games. Final database contained 613,333 tweets covering time span from November 1, 2013 until March 31, 2014. Using hash tags the data were classified into the following categories: Events (21%); News (14%); Sports (12%); Anticipation of the Games (12%); Cheering of the teams (6%) and Problems &amp;amp; Politics (2%). Research reveals considerable differences in the outcomes of machine sentiment classifiers: Deeply Moving, Pattern, and SentiStrength. SentiStrength produced the most suitable results in terms of minimization of incorrectly classified tweets. Methodological implications and directions for future research are discussed.
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Zulkarnain, Zulkarnian, Isti Surjandari, and Reggia Aldiana Wayasti. "Sentiment Analysis for Mining Customer Opinion on Twitter: A Case Study of Ride-Hailing Service Provider." In 2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018. http://dx.doi.org/10.1109/icisce.2018.00113.

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Hakimi, Fajar Darwis Dzikril, Ahmad Zainul Hamdi, Nurissaidah Ulinnuha, Ahmad Hanif Asyhar, and Yuniar Farida. "Analysis of Public Sentiment towards East Java Governor Election 2018 on Twitter using Text Mining." In Built Environment, Science and Technology International Conference 2018. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008905902620267.

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Perez Cabañero, Carmen, Enrique Bigne, Carla Ruiz Mafe, and Antonio Carlos Cuenca. "Sentiment Analysis of Twitter in Tourism Destinations." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11621.

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Given the importance of electronic word of mouth (eWOM), this paperanalyses the content of messages generated by users related to a touristdestination and shared through Twitter. We propose three research questionsregarding eWOM behaviour in Twitter focused on the expertise of thereviewer, sentiment analysis of a tweet and its content. In order to addressthose research questions we carry out text mining analysis by retrievingexisting information on Twitter (over 1500 tweets) regarding to Venice as atourist destination.
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