Relevant bibliographies by topics / Sentiment Analysis

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Academic literature on the topic 'Sentiment Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 March 2025

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Journal articles on the topic "Sentiment Analysis"

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Haidar, Abdullah, and Putri Oktavia Rusadi. "A Sentiment Analysis: History of Islamic Economic Thought." Journal of Islamic Economics (JoIE) 2, no. 2 (October 31, 2022): 150–63. http://dx.doi.org/10.21154/joie.v2i2.5082.

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This study reviews the history of Islamic economic thought research in Islamic economics and finance. It uses descriptive statistical analysis based on selected 125 article publications. The entire sample publications have been published from 1984 to 2022. This study analyzes the number of publications based on journal and year, the top authors, the top-cited paper, and the sentiment analysis. The results show that the research of the history of Islamic economic thought throughout the world has a high-positive sentiment of 1%, a positive sentiment of 27%, a negative sentiment of 33%, a high-negative sentiment of 1%, and the rest have a neutral sentiment of 38%. Also, the number of sentiments for these studies has increased in the world community; the most significant number of high-positive sentiments occurred in 2021, with one publication sentiment. Then the most significant number of positive sentiments occurred in 2019, with as many as seven published article sentiments. The most significant number of neutral sentiments occurred in 2018, the same as positive sentiments, seven published article sentiments, and the most significant number of negative sentiments occurred in 2020, six published article sentiments.Penelitian ini mencoba mengkaji sejarah penelitian pemikiran ekonomi Islam di bidang ekonomi dan keuangan Islam. Ini menggunakan analisis statistik deskriptif berdasarkan 125 publikasi artikel yang dipilih. Seluruh sampel publikasi telah diterbitkan dari tahun 1984 hingga 2022. Studi ini menganalisis jumlah publikasi berdasarkan jurnal dan tahun, penulis teratas, makalah yang dikutip teratas, dan analisis sentimen. Hasil penelitian menunjukkan bahwa penelitian sejarah pemikiran ekonomi Islam di seluruh dunia memiliki sentimen positif tinggi 1%, sentimen positif 27%, sentimen negatif 33%, sentimen negatif tinggi 1%, dan selebihnya. memiliki sentimen netral sebesar 38%. Selain itu, jumlah sentimen untuk studi ini telah meningkat di masyarakat dunia, jumlah sentimen positif tinggi terbesar terjadi pada tahun 2021 dengan satu sentimen artikel publikasi. Kemudian jumlah sentimen positif terbesar terjadi pada tahun 2019, yaitu sebanyak tujuh artikel sentimen yang dipublikasikan. Jumlah sentimen netral terbesar terjadi pada tahun 2018, sama dengan sentimen positif yaitu sebanyak tujuh sentimen artikel yang dipublikasikan, dan jumlah sentimen negatif terbesar terjadi pada tahun 2020 yaitu sebanyak enam sentimen artikel yang dipublikasikan.
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Datt, Jivat Singh. "SENTIMENT ANALYSIS USING CUSTOMER FEEDBACK." International Journal of Trendy Research in Engineering and Technology 07, no. 04 (2023): 09–13. http://dx.doi.org/10.54473/ijtret.2023.7402.

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This Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present customer feedback reviews on products, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behavior.
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Li, Jiangfeng, Ziyu Li, Xiaofeng Ma, Qinpei Zhao, Chenxi Zhang, and Gang Yu. "Sentiment Analysis on Online Videos by Time-Sync Comments." Entropy 25, no. 7 (July 2, 2023): 1016. http://dx.doi.org/10.3390/e25071016.

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Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.
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Li, Fangtao, Minlie Huang, and Xiaoyan Zhu. "Sentiment Analysis with Global Topics and Local Dependency." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1371–76. http://dx.doi.org/10.1609/aaai.v24i1.7523.

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With the development of Web 2.0, sentiment analysis has now become a popular research problem to tackle. Recently, topic models have been introduced for the simultaneous analysis for topics and the sentiment in a document. These studies, which jointly model topic and sentiment, take the advantage of the relationship between topics and sentiment, and are shown to be superior to traditional sentiment analysis tools. However, most of them make the assumption that, given the parameters, the sentiments of the words in the document are all independent. In our observation, in contrast, sentiments are expressed in a coherent way. The local conjunctive words, such as “and” or “but”, are often indicative of sentiment transitions. In this paper, we propose a major departure from the previous approaches by making two linked contributions. First, we assume that the sentiments are related to the topic in the document, and put forward a joint sentiment and topic model, i.e. Sentiment-LDA. Second, we observe that sentiments are dependent on local context. Thus, we further extend the Sentiment-LDA model to Dependency-Sentiment-LDA model by relaxing the sentiment independent assumption in Sentiment-LDA. The sentiments of words are viewed as a Markov chain in Dependency-Sentiment-LDA. Through experiments, we show that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment-LDA is an effective approach for sentiment analysis.
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Ginabila, Ginabila, and Ahmad Fauzi. "Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine." Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 6, no. 2 (July 20, 2023): 111–22. http://dx.doi.org/10.47324/ilkominfo.v6i2.180.

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Abstrak: Manusia memiliki kebutuhan preferensi musik yang yang sangat beragam, oleh karena itu pemutar musik online menjadi salah satu solusi untuk memenuhi kebutuhan ini dengan menyediakan katalog musik yang luas. Analisis sentimen adalah proses untuk mengevaluasi dan mengklasifikasikan sentimen atau perasaan di balik teks atau data yang diberikan. Dalam konteks ini, analisis sentimen dilakukan pada pemutar musik online Spotify. Dua algoritma yang umum digunakan untuk analisis sentimen adalah Naive Bayes dan Support Vector Machine (SVM). Kedua algoritma ini dapat diterapkan dalam analisis sentimen pada pemutar musik online. Data teks seperti ulasan atau komentar pengguna dikumpulkan dan dilabeli dengan sentimen yang sesuai. Hasil dari penelitian menggunakan kedua algoritma ini menghasilkan nilai akurasi yang hampir sama baiknya. Algoritma Support Vector Machine menghasilkan tingkat akurasi sebesar 82,42%, sedangkan untuk Algoritma Naive Bayes mencapai 84,73%.Kata kunci: Analisis Sentimen, Naive Bayes, Support Vector MachineAbstract: Humans have diverse music preferences and online music players are a solution to meet these needs by providing a wide music catalog. Sentiment analysis is the process of evaluating and classifying sentiments or feelings behind given texts or data. In this context, sentiment analysis is performed on Spotify online music players. Two common algorithms used for sentiment analysis are Naive Bayes and Support Vector Machine (SVM). Both algorithms can be applied in sentiment analysis for online music players. Text data such as user reviews or comments are collected and labeled with corresponding sentiments. The results of the research using both algorithms yielded similar high accuracy. The Support Vector Machine algorithm achieved an accuracy rate of 82.42%, while the Naive Bayes algorithm reached 84.73%.Keywords: Sentiment Analysis, Naive Bayes, Support Vector Machine
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Tittu T, Anoush, Rakshitha K, Nanditha TN, Sireya Rani M, and Yukthi S R. "STOCK MARKET ANALYSIS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 15, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28108.

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The abstract aims to address the correlation between stock market movements and public sentiments expressed on Twitter. It delves into the utilization of sentiment analysis and supervised machine learning techniques to explore this connection. The study leverages Word2vec for textual representation, examining how shifts in stock prices align with sentiments expressed in tweets about specific companies. The investigation underscores the potential impact of positive news and social media sentiments on stock prices, emphasizing a demonstrated correlation between fluctuations in stock prices and sentiments conveyed in Twitter.- Keywords: Hashtag Collection, Data Collection, Real-Time Stock History Data, Positive Keywords, Negative Keywords, Polarity Computation, Sentiment Analysis , Sentiment Index Computation, Sentiment Discrepancy Index, Price Prediction, Yahoo Finance API
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K. Divya and Mrs P. Menaka. "Twitter Sentiment Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (March 15, 2025): 1305–10. https://doi.org/10.32628/cseit25112465.

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Sentiment analysis deals with relating and classifying opinions or sentiments expressed in source textbook. A significant quantum of sentiment-rich data is being produced via social media in the form of tweets, status updates, blog entries, and other content. Understanding the opinions of the millions can be greatly served from sentiment analysis of this stoner- generated data. Twitter sentiment analysis is more grueling than general sentiment analysis because of the frequence of misspellings and shoptalk expressions. Twitter allows a character count of over to 140 characters. The two approaches employed to assay sentiments from the textbook are the knowledge base fashion and the machine literacy approach. Analysing sentiments help in understanding how people are allowing emotionally and classifying it as negative, positive or neutral. The dataset used is a collection of tweets related to the brand apple. Two different machine learning classifiers are used then, so that a person's sentiment can be linked. These classifiers are applied and also the stylish classifier with the stylish result will be chosen in order to prognosticate people's feelings. Professionals will be better suitable to assess people's feelings and fete early signs of torture through this analysis.
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Karuna, G., Pavuluri Anvesh, Chiranji Sharath Singh, Kommula Ruthvik Reddy, Praveen Kumar Shah, and S. Siva Shankar. "Feasible Sentiment Analysis of Real Time Twitter Data." E3S Web of Conferences 430 (2023): 01045. http://dx.doi.org/10.1051/e3sconf/202343001045.

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Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.
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Qu, Saiying. "A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis." Journal of Electrical Systems 20, no. 6s (April 29, 2024): 1575–86. http://dx.doi.org/10.52783/jes.3076.

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A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.
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Sunil Kumar, V., Vedashree C.R, and Sowmyashree S. "IMAGE SENTIMENTAL ANALYSIS: AN OVERVIEW." International Journal of Advanced Research 10, no. 03 (March 31, 2022): 361–70. http://dx.doi.org/10.21474/ijar01/14398.

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Visual content, such as photographs and video, contains not only objects, locations, and events, but also emotional and sentimental clues. On social networking sites, images are the simplest way for people to communicate their emotions. Images and videos are increasingly being used by social media users to express their ideas and share their experiences. Sentiment analysis of such large-scale visual content can aid in better extracting user sentiments toward events or themes, such as those in image tweets, so that sentiment prediction from visual content can be used in conjunction with sentiment analysis of written content. Despite the fact that this topic is relatively new, a wide range of strategies for various data sources and challenges have been created, resulting in a substantial body of study. This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises. A description of new obstacles is also included, as well as an assessment of progress toward more sophisticated systems and related practical applications, as well as a summary of the studys findings.
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Dissertations / Theses on the topic "Sentiment Analysis"

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Kavaliauskas, Vytautas. "Nuomonių analizės taikymas komentarams lietuvių kalboje." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110615_130252-94422.

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Pastaruosius keletą metų, žmonėms vis aktyviau pradėjus reikšti savo požiūrį, įsitikinimus ir potyrius internete, susiformavo nauja tyrinėjimų sritis, kuri apima nuomonių gavybą ir sentimentų analizę. Šios srities tyrinėjimus aktyviai skatina ir jais domisi įvairios verslo kompanijos, matančios didelį, dėka nuolat tobulėjančių rezultatų, praktinį potencialą. Šis darbas skirtas apžvelgti teorinius bei praktinius nuomonės gavybos ir sentimentų analizės rezultatus bei realizuoti prototipinę nuomonės analizės sistemą, skirtą tyrinėti trumpus komentarus, parašytus lietuvių kalba. Taip pat darbe aprašomos problemos, susijusios su lietuvių kalbos taikymu nuomonės gavybos ir sentimentų analizės sistemų veikloje. Galiausiai, baigiamojoje dalyje suformuluojami ir išdėstomi rekomendacinio pobūdžio etapai, skirti nuomonės analizės sistemų kūrimui bei tobulinimui.
In past few years, more and more people started to express their views, beliefs and experiences on the Internet. This caused the emergence of a new research field, which includes opinion mining and sentiment analysis. Various business companies are actively interested in researches of this domain and seeing big potential for practical adaptation of the results. This Master Thesis covers the review of theoretical and practical results of opinion mining and sentiment analysis, including attempt of creating prototype system for opinion analysis of comments in Lithuanian. Also this study aims to identify problems related to adaptation of Lithuanian language in opinion mining and sentiment analysis system work. Finally, last part contains of the formulated guidance steps for development and improvement of the opinion mining and sentiment analysis.
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Bao, Lingxian. "Sentiment induction for attention and lexicon regularized neural sentiment analysis: improving aspect-based neural sentiment analysis with lexicon enhancement, attention regularization and sentiment induction." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673363.

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Deep neural networks as an end-to-end approach lacks flexibility and robustness from an application point of view, as one cannot easily adjust the network to fix an obvious problem, especially when new training data is not available: e.g. when the model consistently predicts positive when seeing the word “disappointed”. Meanwhile, it is less stressed that the attention mechanism is likely to “over-focus” on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this thesis, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader “focus” on the input sequence. Moreover, we try to further improve the proposed lexicon enhanced neural sentiment analysis system by applying sentiment domain adaptation.
Las redes neuronales profundas como enfoque integral carecen de flexibilidad y robustez desde el punto de vista de la aplicación, ya que no se puede ajustar fácilmente la red para solucionar un problema evidente, especialmente cuando no se dispone de nuevos datos de entrenamiento: por ejemplo, cuando el modelo predice sistemáticamente positivo al ver la palabra ”decepcionado”. Por otro lado, se hace menos hincapié en que es probable que el mecanismo de atención "se concentre demasiado” en partes concretas de una oración, mientras ignora posiciones que proporcionan información clave para juzgar la polaridad. En esta tesis, describimos un enfoque sencillo pero eficaz para aprovechar la información del léxico de modo que el modelo sea maás flexible y robusto. También exploramos el efecto de regularizar los vectores de atención para permitir que la red tenga un ”enfoque” más amplio en la secuencia de entrada. Además, tratamos de mejorar aún más el sistema que proponemos de análisis profundo de sentimiento con el soporte de léxico aplicando sobre el mismo la adaptación del anàlisis de sentimiento al dominio.
深度神经网络作为一种端到端的方法，从应用的角度来看缺乏灵 活性和鲁棒性。例如，当模型看到词语“失望”却始终预测正值时， 在没有新的训练数据的情况下，很难轻易地通过调整模型来解决问 题。另外，常用的注意力机制可能会“过度关注”句子的某些特定部 分，从而忽略能提供判断极性关键信息的位置；此情况在业界鲜有 提及。在本文中，我们描述一种简单却行之有效的方法将词典信息 与深度神经网络相结合，从而改进模型的灵活性及鲁棒性。我们亦 探索通过正则化注意力向量来抑制注意力机制“过度关注”的问题。 此外，我们尝试通过应用情感域自适应来进一步改进所提出的词典 增强型神经情感分析系统。
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Erogul, Umut. "Sentiment Analysis In Turkish." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610616/index.pdf.

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Sentiment analysis is the automatic classification of a text, trying to determine the attitude of the writer with respect to a specific topic. The attitude may be either their judgment or evaluation, their feelings or the intended emotional communication. The recent increase in the use of review sites and blogs, has made a great amount of subjective data available. Nowadays, it is nearly impossible to manually process all the relevant data available, and as a consequence, the importance given to the automatic classification of unformatted data, has increased. Up to date, all of the research carried on sentiment analysis was focused on English language. In this thesis, two Turkish datasets tagged with sentiment information is introduced and existing methods for English are applied on these datasets. This thesis also suggests new methods for Turkish sentiment analysis.
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Cheng, Tai Wai David. "Corpus and sentiment analysis." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/2744/.

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Information extraction/retrieval has been of interest to researchers since the early 1960's. A series of conferences and competitions have been held by DARPA/NIST since the late 1980's has resulted in the analysis of news reports and government reports in English and other languages, notably Chinese and Arabic. A number of methods have been developed for analysing `free' natural language texts. Furthermore, a number of systems for understanding messages have been developed, focusing on the area of named entity extraction, templates for dealing with certain kinds of news. The templates were handcrafted, and a lot of ad-hoc knowledge went into the creation of such systems. Seven of these systems have been reviewed. Despite the fact that IE systems built for different tasks often differ from each other, the core elements are shared by nearly every extraction system. Some of these core elements such as parser and part of speech (POS) tagger, are tuned for optimal performance for a specific domain, or text with pre-defined structures. The extensive use of gazetteers and manually crafted grammar rules further limits the portability of the existing IE systems to work language and domain independently. The goal of this thesis is to develop an algorithm that can be used to extract information from free texts, in our case, from financial news text; and from arbitrary domains unambiguously. We believe the use of corpus linguistics and statistical techniques would be more appropriate and efficient for this task, instead of using other approaches that rely on machine learning, POS taggers, parsers, and so on, which are tuned to work for a predefined domain. Based on this belief, a framework using corpus linguistics and statistical techniques, to extract information as unambiguously as possible from arbitrary domains was developed. A contrastive evaluation has been carried out not only in the domain of financial texts and movie reviews, but also with multi-lingual texts (Chinese and English). The results are encouraging. Our preliminarily evaluation, based on the correlation between a time series of positive (negative) sentiment word (phrase) counts with a time series of indices produced by stock exchanges (Financial Times Stock Exchange, Dow Jones Industrial Average, Nasdaq, S&P 500, Hang Seng Index, Shanghai Index, and Shenzhen Index) showed that when the positive (negative) sentiment series correlates with the stock exchange index, the negative (positive) shows a smaller degree of correlation and in many cases a degree of anti-correlation. Any interpretation of our result requires a careful econometrically well grounded analysis of the financial time series - this is beyond the scope of this work.
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Niccum, Cameron Michael. "Sentiment Analysis using Tensor2Tensor." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2440.

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Tensor2Tensor is a library of deep learning models and datasets developed by the Google Brain team. The transformer models of this library are implemented using stacked self-attention layers instead of containing recurrent layers. Two of these transformer models have been chosen along with a few hyperparameter sets for analyzing the Internet Movie Database (IMDB). The runtime and accuracy of these models are then compared to themselves as well as the accuracy of previously known models.
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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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Filho, Pedro Paulo Balage. "Aspect extraction in sentiment analysis for portuguese language." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122017-140435/.

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Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approaches. In addition, richer linguistic knowledge is also explored by using syntatic dependencies and semantic roles, leading to better results. This work lead to the establishment of new benchmarks for the aspect extraction in Portuguese.
A análise do sentimento orientada a aspectos é o campo de estudo que extrai e interpreta o sentimento, geralmente classificado como positivo ou negativo, em direção a algum alvo ou aspecto em um texto de opinião. Esta tese de doutorado detalha um estudo empírico de técnicas e métodos para extração de aspectos em análises de sentimentos baseadas em aspectos com foco na língua Portuguesa. Foram exploradas três diferentes abordagens: métodos baseados na frequências, métodos baseados na relação e métodos de aprendizagem de máquina. Em cada abordagem, este trabalho mostra um estudo comparativo entre um córpus para o Português e outro para o Inglês e as diferenças encontradas na aplicação destas abordagens. Além disso, o conhecimento linguístico mais rico também é explorado pelo uso de dependências sintáticas e papéis semânticos, levando a melhores resultados. Este trabalho resultou no estabelecimento de novos padrões de avaliação para a extração de aspectos em Português.
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Gaudette, Lisa. "Compact features for sentiment analysis." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28295.

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This work examines a novel method of developing features to use for machine learning of sentiment analysis and related tasks. This task is frequently approached using a Bag of Words representation -- one feature for each word encountered in the training data -- which can easily number in the thousands or tens of thousands. This thesis develops a set of "numeric" features, by learning scores for words, dividing the range of possible scores into a number of bins, and then generating features based on counting how many words in each document have scores in each bin. This allows for effective learning of sentiment and related tasks with 25 features; in fact, performance was very often slightly better with these features. This reduction in the number of features allows for the processing of much larger collections of texts than previously attempted. In addition, we carefully consider the problem of evaluating ordinal problems.
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Athar, Awais. "Sentiment analysis of scientific citations." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.707942.

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Smith, Phillip. "Sentiment analysis of patient feedback." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7406/.

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The application of sentiment analysis as a method for the automatic categorisation of opinions in text has grown increasingly popular across a number of domains over the past few years. In particular, health services have started to consider sentiment analysis as a solution for the task of processing the ever-growing amount of feedback that is received in regards to patient care. However, the domain is relatively under-studied in regards to the application of the technology, and the effectiveness and performance of methods have not been substantially demonstrated. Beginning with a survey of sentiment analysis and an examination of the work undertaken so far in the clinical domain, this thesis examines the application of supervised machine learning models to the classification of sentiment in patient feedback. As a starting point, this requires a suitably annotated patient feedback dataset, for both analysis and experimentation. Following the construction and detailed analysis of such a resource, a series of machine learning experiments study the impact of different models, features and review types to the problem. These experiments examine the applicability of the selected methods and demonstrate that model and feature choice may not be a significant issue in sentiment classification, whereas the type of review that the models train and test across does affect the outcome of classification. Finally, by examining the role that responses play in the patient feedback process and developing the idea of incorporating the inter-document context provided by the response into the feedback classification process, a recalibration framework for the labelling of sentiment in ambiguous texts for patient feedback is developed. As this detailed analysis will demonstrate, while some problems in performance remain despite the development and implementation of the recalibration framework, sentiment analysis of patient feedback is indeed viable, and achieves a classification accuracy of 91.4% and F1 of 0.902 on the gathered data. Furthermore, the models and data can serve as a baseline to study the nature of patient feedback, and provide a unique opportunity for the development of sentiment analysis in the clinical domain.
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Books on the topic "Sentiment Analysis"

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Poria, Soujanya, Amir Hussain, and Erik Cambria. Multimodal Sentiment Analysis. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4.

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Kahn, Michael N. Sentiment market analysis. Upper Saddle River, N.J: FTPress Delivers, 2010.

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Nandal, Neha, Rohit Tanwar, and Varun Sapra. Sentiment Analysis Unveiled. Boca Raton: CRC Press, 2025. https://doi.org/10.1201/9781003504832.

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Xu, Hua. Multi-Modal Sentiment Analysis. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5776-7.

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Liu, Bing. Sentiment Analysis and Opinion Mining. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9.

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Thelwall, Mike, and Helene Snee. How to Conduct Sentiment Analysis. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529607406.

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Ceron, Andrea, Luigi Curini, and Stefano M. Iacus. Social Media e Sentiment Analysis. Milano: Springer Milan, 2014. http://dx.doi.org/10.1007/978-88-470-5532-2.

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Ahmad, Khurshid, ed. Affective Computing and Sentiment Analysis. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1757-2.

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Pedrycz, Witold, and Shyi-Ming Chen, eds. Sentiment Analysis and Ontology Engineering. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30319-2.

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Kagan, Vadim, Edward Rossini, and Demetrios Sapounas. Sentiment Analysis for PTSD Signals. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-3097-1.

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Book chapters on the topic "Sentiment Analysis"

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Silaparasetty, Vinita. "Sentiment Analysis." In Deep Learning Projects Using TensorFlow 2, 87–118. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5802-6_4.

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Dominik, Hofer. "Sentiment Analysis." In Data Science – Analytics and Applications, 111–12. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-19287-7_17.

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Zadrozny, Peter, and Raghu Kodali. "Sentiment Analysis." In Big Data Analytics Using Splunk, 255–82. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5762-2_14.

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L. Jockers, Matthew, and Rosamond Thalken. "Sentiment Analysis." In Text Analysis with R, 159–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_14.

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Luo, Tiejian, Su Chen, Guandong Xu, and Jia Zhou. "Sentiment Analysis." In Trust-based Collective View Prediction, 53–68. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7202-5_4.

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Biagioni, Raoul. "Sentiment Analysis." In The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts, 7–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38971-4_2.

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Sarkar, Dipanjan. "Sentiment Analysis." In Text Analytics with Python, 567–629. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_9.

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Chen, Hsinchun. "Sentiment Analysis." In Dark Web, 171–201. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1557-2_10.

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Thelwall, Mike. "Sentiment Analysis." In The SAGE Handbook of Social Media Research Methods, 545–56. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2016. http://dx.doi.org/10.4135/9781473983847.n32.

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Kumar, Sunil. "Sentiment Analysis." In Python for Accounting and Finance, 265–300. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54680-8_17.

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Conference papers on the topic "Sentiment Analysis"

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Aggrawal, Aditi, and Deepika Varshney. "Multimodal Sentiment Analysis: Perceived vs Induced Sentiments." In 2024 Silicon Valley Cybersecurity Conference (SVCC), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/svcc61185.2024.10637377.

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Rishu, Amanveer Singh, and Sarvesh Tanwar. "Unveiling Sentiments: CNN-LSTM Based Social Media Sentiment Analysis." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673560.

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Rathore, Saurabh Pratap Singh, Jayashree Patole, Geetali Tilak, ReenaMahapatra Lenka, Joe Cajetan Lopez, and Priyanka. "Consumer Sentiment Analysis." In 2024 International Conference on Smart Devices (ICSD), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icsd60021.2024.10751293.

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Kant, Shashi, Raushan Kumar Upadhyay, Palak Gupta, Raghav Tiwari, and Honey Kumar. "Youtube Comments Sentiment Analysis." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 570–74. IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895870.

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xuyang, Wang, and shi haojun. "Attention fusion sentiment analysis with cross-modal sentiment augmentation." In Third International Conference on Machine Vision, Automatic Identification and Detection, edited by Renchao Jin, 106. SPIE, 2024. http://dx.doi.org/10.1117/12.3036317.

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Medeiros, Murilo C., and Vinicius R. P. Borges. "Tweet Sentiment Analysis Regarding the Brazilian Stock Market." In VIII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2019.6550.

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This paper describes a methodology for analyzing sentiments and for knowledge discovery in tweets regarding the Brazilian stock market. The proposed methodology starts by preprocessing and characterizing tweets to obtain an associated vector-space model. After that, a dimensionality reduction is em- ployed by using Principal Component Analysis and t-Stochastic Neighbor Embedding. Sentiment analysis of stock market tweets is performed by considering the tasks of sentiment classification, topic modeling and clustering, along with a visual analysis process. Experiments results showed satisfactory performances in single and multi-label sentiment classification scenarios. The visual analysis process also revealed interesting relationships among topics and clusters.
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Bu, Kun, and Kandethody Ramachandran. "Comparative Analysis of Sentiment in Original and Summarized Tweets: Leveraging Transformer Models for Enhanced NLP Insights." In 5th International Conference on Artificial Intelligence and Big Data (AIBD 2024). Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140404.

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This paper investigates the sentiments of Twitter users towards the emergent topic of ChatGPT, leveraging advanced techniques in natural language processing (NLP) and sentiment analysis (SA). Our approach uniquely incorporates a dual setting for sentiment analysis: one analyzes the sentiments of original, full-length tweets, while the other first condenses these tweets into succinct summaries before performing sentiment analysis. By employing this dual approach, we are able to offer a comparative analysis of sentiment assessment pre- and post-text summarization, exploring the accuracy and reliability of the summarized sentiments. Central to our methodology is the application of Transformer models, specifically ProphetNet, which facilitates a deeper and more nuanced understanding of the original text. Unlike traditional methods that rely on keyword extraction and aggregation, our approach generates coherent and contextually rich summaries, providing a novel lens for sentiment analysis. This research contributes to the field by presenting a comprehensive study comparing sentiment analysis outcomes between original texts and their summarized counterparts, and examining the effectiveness of different NLP techniques, namely NLTK and the Transformer-based ProphetNet model. The findings offer valuable insights into the dynamics of sentiment analysis in the context of social media and the efficacy of state-of-the-art NLP technologies in processing complex, real-world data.
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Thapa, Bipun. "Sentiment Analysis of Cyber Security Content on Twitter and Reddit." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120708.

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Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.
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Tsiligaridis, John. "Approaches of Classification Models for Sentiment Analysis." In 5th International Conference on Advanced Natural Language Processing. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141007.

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Sentiment analysis (SA) is a Natural Language Processing (NLP) method that helps identify the emotions in text. It is the automated process of identifying and classifying emotions in a text as positive, negative, or neutral sentiment. This way, companies can understand customers’ sentiments, improve their products and services accordingly, and determine effective strategies. The need to discover the algorithm with the best classification performance is obvious. To this end, two different approaches for Sentiment Analysis problems are presented. The first one is based on Machine Learning (ML) models and the second one on Deep Learning (DP) models. Most ML models are flexible depending on their classifier hyperparameters and provide competitive accuracy levels but not all of them. Logistic Regression (LR), Random Forests (RF) of ML and the various models based on Neural Networks (NNs) of DL are applied. Useful results are obtained. Measures for classifiers’ effectiveness are also provided.
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Arora, Adwita, Krish Chopra, Divya Chaudhary, Ian Gorton, and Bijendra Kumar. "Sentiment Analysis of Social Media Data on COVID-19." In 4th International Conference on Natural Language Processing and Machine Learning. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130802.

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The COVID-19 pandemic has forced people to resort to social media to express their thoughts and opinions, which could be analysed further. In this paper, we aim to analyse the impact of the COVID-19 pandemic on social media users by Sentiment analysis of data collected from popular social media platforms, Twitter and Reddit. The textual data is preprocessed and is made fit for proper sentiment analysis using two unsupervised methods, VADER and TextBlob. Special care is taken to translate tweets or comments not in the English language to ensure their proper classification. We perform a comprehensive analysis of the emotions of the users specific to the COVID pandemic along with a time-based analysis of the trends, and a comparison of the performance of both the tools used. Geographical distribution of the sentiments is also done to see how they vary across regional boundaries.
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Reports on the topic "Sentiment Analysis"

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Shellman, Stephen M., and Michael A. Covington. Automated Sentiment Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada532194.

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Chitla, Pravalika Ravikumar. Sentiment Analysis of Reviews. Ames (Iowa): Iowa State University, January 2021. http://dx.doi.org/10.31274/cc-20240624-1275.

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Hart, Laurel. The Linguistics of Sentiment Analysis. Portland State University Library, May 2013. http://dx.doi.org/10.15760/honors.19.

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Hua, Tianyu. Machine learning for sentiment analysis: Opportunities and challenges. Ames (Iowa): Iowa State University, May 2022. http://dx.doi.org/10.31274/cc-20240624-974.

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Vizmanos, Jana Flor, Sheila Siar, Jose Ramon Albert, Janina Luz Sarmiento, and Angelo Hernandez. Like, Comment, and Share: Analyzing Public Sentiments of Government Policies in Social Media. Philippine Institute for Development Studies, December 2023. http://dx.doi.org/10.62986/dp2023.33.

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Social media has become an increasingly important tool for gauging public sentiment, offering real-time insights that can guide policy decisions. This study focuses on analyzing sentiments expressed on the Philippine Institute for Development Studies (PIDS) Facebook page, providing a window into public opinion on various development issues and governmental policies. By conducting opinion mining and sentiment analysis on comments from the top three viral Facebook posts of PIDS, which discuss education, the middle class, and social protection policies, the study reveals a range of public perspectives and highlights the challenges faced by the populace. Additionally, an online survey targeting PIDS' social media followers was conducted to understand their demographics and preferences in accessing development research. The findings demonstrate the effectiveness of social media analytics in capturing genuine public opinion, which can be instrumental in refining policies based on evidence. The study recommends enhancing analytics capabilities, systematically incorporating these insights while safeguarding data privacy, and continuously updating strategies to reflect changing public sentiments. This policy research study underscores the value of social media data in making governance more responsive and inclusive.
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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. Gaithersburg, MD: National Institute of Standards and Technology, April 2019. http://dx.doi.org/10.6028/nist.cswp.04222019.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. Gaithersburg, MD: National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.8.

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Jain, Kavita. Analyzing customer reactions towards popular Airlines with Aspect-based Sentiment Analysis. Ames (Iowa): Iowa State University, January 2021. http://dx.doi.org/10.31274/cc-20240624-210.

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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, José Luis González, Ana Isabel Hernáez, José Luis González, Jorge Quintana, and Javier Tarancón. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Madrid: Banco de España, March 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the corporate level to detect sentiment anomalies for specific corporations (idiosyncratic risks), while employing a wide range of network metrics to monitor systemic risks. In the realm of supervisory technology (SupTech), anomaly detection in sentiment analysis serves as a proactive tool for financial authorities. By continuously monitoring sentiment trends, SupTech applications can provide early warnings of potential financial distress or systemic risks.
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Liang, Xiao. Analyzing the Amazon Shopping Experience: A Sentiment Analysis Based on Natural Language Processing (NLP) and Model Comparison. Ames (Iowa): Iowa State University, May 2024. http://dx.doi.org/10.31274/cc-20240624-215.

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