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Wang, Xinzhi, Hui Zhang, and Zheng Xu. "Public Sentiments Analysis Based on Fuzzy Logic for Text." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (November 2016): 1341–60. http://dx.doi.org/10.1142/s0218194016400076.
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Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.
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Hung, Chihli, and You-Xin Cao. "Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts." Electronic Library 38, no. 1 (November 25, 2019): 155–69. http://dx.doi.org/10.1108/el-04-2019-0093.
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Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.
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Chandra, Rohitash, and Aswin Krishna. "COVID-19 sentiment analysis via deep learning during the rise of novel cases." PLOS ONE 16, no. 8 (August 19, 2021): e0255615. http://dx.doi.org/10.1371/journal.pone.0255615.
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Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.
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Prasad, Guru, Amith K. Jain, Prithviraj Jain, and Nagesh H. R. "A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis." International Journal of Open Source Software and Processes 10, no. 4 (October 2019): 44–59. http://dx.doi.org/10.4018/ijossp.2019100103.
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Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.
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Sharayu, Athare, and Rathod Vijay. "Novel Sentiment Analysis using Twitter." International Journal of Computer Applications 182, no. 40 (February 15, 2019): 7–9. http://dx.doi.org/10.5120/ijca2019918429.
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Orr, Leah. "Defoe, Sentiment, and the Novel." Eighteenth-Century Life 42, no. 3 (September 1, 2018): 37–41. http://dx.doi.org/10.1215/00982601-6988718.
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Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. "Estimate Sentiment of Crowds from Social Media during City Events." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 21, 2019): 836–50. http://dx.doi.org/10.1177/0361198119846461.
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City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.
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Jha, Vandana, Savitha R, P. Deepa Shenoy, Venugopal K R, and Arun Kumar Sangaiah. "A novel sentiment aware dictionary for multi-domain sentiment classification." Computers & Electrical Engineering 69 (July 2018): 585–97. http://dx.doi.org/10.1016/j.compeleceng.2017.10.015.
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Aiyanyo, Imatitikua D., Hamman Samuel, and Heuiseok Lim. "Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning." Sustainability 13, no. 9 (April 29, 2021): 4986. http://dx.doi.org/10.3390/su13094986.
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In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics.
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Murfi, Hendri, Furida Lusi Siagian, and Yudi Satria. "Topic features for machine learning-based sentiment analysis in Indonesian tweets." International Journal of Intelligent Computing and Cybernetics 12, no. 1 (February 28, 2019): 70–81. http://dx.doi.org/10.1108/ijicc-04-2018-0057.
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Purpose The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets. Design/methodology/approach Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class. Findings The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis. Originality/value The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.
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Wang, Kai, and Yu Zhang. "Topic Sentiment Analysis in Online Learning Community from College Students." Journal of Data and Information Science 5, no. 2 (May 20, 2020): 33–61. http://dx.doi.org/10.2478/jdis-2020-0009.
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AbstractPurposeOpinion mining and sentiment analysis in Online Learning Community can truly reflect the students’ learning situation, which provides the necessary theoretical basis for following revision of teaching plans. To improve the accuracy of topic-sentiment analysis, a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approachWe aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularity-levels. The proposed method comprised data preprocessing, topic detection, sentiment analysis, and visualization.FindingsThe proposed model can effectively perceive students’ sentiment tendencies on different topics, which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitationsThe model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach, without considering the intensity of students’ sentiments and their evolutionary trends.Practical implicationsThe implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint, which can be utilized as a reference for enhancing the accuracy of learning content recommendation, and evaluating the quality of their services.Originality/valueThe topic-sentiment analysis model can clarify the hierarchical dependencies between different topics, which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.
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Nurrohmat, Muh Amin, and Azhari SN. "Sentiment Analysis of Novel Review Using Long Short-Term Memory Method." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 3 (July 31, 2019): 209. http://dx.doi.org/10.22146/ijccs.41236.
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The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.
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Muppidi, Satish, Satya Keerthi Gorripati, and B. Kishore. "An approach for bibliographic citation sentiment analysis using deep learning." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (January 18, 2021): 353–62. http://dx.doi.org/10.3233/kes-200087.
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Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.
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Dashtipour, Kia, Mandar Gogate, Ahsan Adeel, Hadi Larijani, and Amir Hussain. "Sentiment Analysis of Persian Movie Reviews Using Deep Learning." Entropy 23, no. 5 (May 12, 2021): 596. http://dx.doi.org/10.3390/e23050596.
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Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.
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Saranya, N., R. Gunavathi, and . "A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier." International Journal of Engineering & Technology 7, no. 4.7 (September 27, 2018): 164. http://dx.doi.org/10.14419/ijet.v7i4.7.20536.
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Sentimental analysis is the process of identifying the human’s thoughts or feelings. So Many methods have been developed for the sentimental analysis. Machine learning is one of the widely used approaches towards sentiment classification. In this work, Sentimental analysis is done by using Relevance Vector Machine Classifier with Cuckoo Search Optimization. Here Relevance Vector Machine Classifier (RVMC) is combined with Cuckoo Search Optimization (CSO) for better accuracy and performance. Experiment is made with movie and twitter datasets. Accuracy, precision and recall of all other techniques are evaluated. Here the comparison is made among other algorithms. The result shows that RVMC-CSO algorithm gives accuracy and good performance than other algorithm like SVM, ELM and RVM.
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Matharasi, P. Bavithra. "SENTIMENT ANALYSIS USING A NOVEL APPROACH TO CLASSIFY SENTIMENTS IN SOCIAL NETWORKING DATA." International Journal of Advanced Research in Computer Science 9, no. 1 (February 20, 2018): 297–301. http://dx.doi.org/10.26483/ijarcs.v9i1.5346.
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Batanović, Vuk, Miloš Cvetanović, and Boško Nikolić. "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts." PLOS ONE 15, no. 11 (November 12, 2020): e0242050. http://dx.doi.org/10.1371/journal.pone.0242050.
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Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.
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Bagheri, Ayoub. "Integrating word status for joint detection of sentiment and aspect in reviews." Journal of Information Science 45, no. 6 (November 19, 2018): 736–55. http://dx.doi.org/10.1177/0165551518811458.
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A crucial task in sentiment analysis is aspect detection: the step of selecting the aspects on which opinions are expressed. This step anticipates the step of determining whether the opinions on aspects are positive or negative. This article proposes a novel probabilistic generative topic model for aspect-based sentiment analysis which is able to discover the latent structure of a large collection of review documents. The proposed joint sentiment-aspect detection model (SAM) is a generative topic model that incorporates the structure of review sentences for detecting aspects and sentiments simultaneously. The intuitions behind the SAM are that from generating documents by latent single- and multi-word topics, modelling the word distribution for each topic and learning of the prior distribution over topics in sentences of documents. SAM introduces word status so that the model can decide when to sample from a bigram distribution or a unigram distribution and integrates all these components into one combined model for aspect-based sentiment analysis. We evaluate SAM both qualitatively and quantitatively to show that the model is indeed able to perform the task effectively and improves significantly over standard joint sentiment-aspect models. The proposed model can easily be transformed between domains or languages and can detect the polarity of text data at various levels. However, for the quantitative analysis, we mainly focus on presenting the results for the document-level sentiment classification.
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Chen, Wen, Zhiyun Xu, Xiaoyao Zheng, Qingying Yu, and Yonglong Luo. "Research on Sentiment Classification of Online Travel Review Text." Applied Sciences 10, no. 15 (July 30, 2020): 5275. http://dx.doi.org/10.3390/app10155275.
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In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.
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Ruiz-Soler, Javier, Luigi Curini, and Andrea Ceron. "Commenting on Political Topics Through Twitter: Is European Politics European?" Social Media + Society 5, no. 4 (October 2019): 205630511989088. http://dx.doi.org/10.1177/2056305119890882.
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The aim of this study was to explore social media, and specifically Twitter’s potential to generate a European demos. Our use of data derived from social media complements the traditional use of mass media and survey data within existing studies. We selected two Twitter hashtags of European relevance, # schengen and # ttip, to test several theories on a European demos (non-demos, European democracy, or pan-European demos) and to determine which of these theories was most applicable in the case of Twitter topics of European relevance. To answer the research question, we performed sentiment analysis. Sentiment analysis performed on data gathered on social media platforms, such as Twitter, constitutes an alternative methodological approach to more formal surveys (e.g., Eurobarometer) and mass media content analysis. Three dimensions were coded: (1) sentiments toward the issue public, (2) sentiments toward the European Union (EU), and (3) the type of framing. Among all of the available algorithms for conducting sentiment analysis, integrated sentiment analysis (iSA), developed by the Blog of Voices at the University of Milan, was selected for the data analysis. This is a novel supervised algorithm that was specifically designed for analyses of social networks and the Web 2.0 sphere (Twitter, blogs, etc.), taking the abundance of noise within digital environments into consideration. An examination and discussion of the results shows that for these two hashtags, the results were more aligned with the demoicracy and “European lite identity” models than with the model of a pan-European demos.
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Qiu, Ningjia, Zhuorui Shen, Xiaojuan Hu, and Peng Wang. "A novel sentiment classification model based on online learning." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261984576. http://dx.doi.org/10.1177/1748302619845764.
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Memory limitation and slow training speed are two important problems in sentiment analysis. In this paper, we propose a sentiment classification model based on online learning to improve the training speed of the sentiment classification. First, combining the adaptive adjustment of learning rate of the Adadelta algorithm and the characteristics of avoid frequent jitter of Adam algorithm in the later stage of training, we present a novel Adamdelta algorithm. It solves the problem that learning rate of traditional follow the regularized leader (FTRL)-Proximal online learning algorithm will disappear with the increase of training times. Moreover, we gain an optimized logistic regression (LR) model and use it to the sentiment classification of online learning. Finally, we compare the proposed algorithm with five similar models with the experimental data of the IMDb movie review dataset. Experimental results show that the improved algorithm has better classification effect and can effectively improve the precision and recall of the classifier.
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Wijayanti, Rini, and Andria Arisal. "Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (April 2021): 1–16. http://dx.doi.org/10.1145/3425632.
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A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet . The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trained in positive and negative sentiment corpus from online marketplaces review and Twitter data. The valence score of each lexicon is recalculated based on its relative occurrence in the corpus. We also add some famous slang words and emoticons to enrich the lexicon. Our experiment shows that the proposed method can provide an increase of 3.5 times lexicon number as well as improve the accuracy of 80.9% for online review and 95.7% for Twitter data, and they are better than other published and available Indonesian sentiment lexicons.
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Yadav, Ashima, and Dinesh Kumar Vishwakarma. "A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–30. http://dx.doi.org/10.1145/3475867.
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Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net) , which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.
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Samuel, Avinash, and Dilip Kumar Sharma. "A Novel Framework for Sentiment and Emoticon-Based Clustering and Indexing of Tweets." Journal of Information & Knowledge Management 17, no. 02 (June 2018): 1850013. http://dx.doi.org/10.1142/s0219649218500132.
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Social Networks have become an important part of people’s life as they share their day-to-day happenings, portray their opinions on various topics or find out information related to their queries. Due to the overwhelming volume of tweets generated on a daily basis, it is not possible to read all the tweets and differentiate the tweets based on the views or the attitude they portray only. The primary objective of sentiment analysis is to find out the attitude/emotion/opinion/sentiment that is present in the material provided. Commonly, the tweets can be clustered on the basis of them being positive or negative i.e. being in favour of the topic or being against the topic. The clustering and indexing of the tweets help in the organisation, searching, and summarisation of task. Twitter data are considered as Big Data and the information contained within the tweets is unstructured and if utilised properly can be very useful for educational and governance purposes. In this paper, a method is presented which clusters and then indexes the tweets on the basis of the sentiments and emoticons that are present in the tweet.
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Zhu, Yinglin, Wenbin Zheng, and Hong Tang. "Interactive Dual Attention Network for Text Sentiment Classification." Computational Intelligence and Neuroscience 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8858717.
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Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.
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Daniel, D. Anand Joseph, and M. Janaki Meena. "A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection." Scalable Computing: Practice and Experience 22, no. 1 (February 9, 2021): 53–66. http://dx.doi.org/10.12694/scpe.v22i1.1839.
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Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.
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Amrani, Yassine Al, Mohamed Lazaar, and Kamal Eddine El Kadiri. "A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4554. http://dx.doi.org/10.11591/ijece.v8i6.pp4554-4567.
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Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.
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Neves, Maria Elisabete Duarte. "Payout and firm’s catering." International Journal of Managerial Finance 14, no. 1 (February 5, 2018): 2–22. http://dx.doi.org/10.1108/ijmf-03-2017-0055.
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Purpose The purpose of this paper is to investigate whether investor sentiments exert significant influence on corporate dividend policy. Additionally it provides further evidence on the moderating role of certain firm’s characteristics on the relation between dividends and investor sentiment. Design/methodology/approach A sample of 635 firms from 12 Eurozone countries for the period of 1986-2003 has been used. A dividend model has been suggested which incorporates a variable at the firm level that proxies for the catering effect, as a measure of investor sentiments. The estimation model of dividends is based on the Generalized Method of Moments (Arellano and Bond, 1991). Findings It can be concluded that psychological factors influence the decision to pay. Furthermore, other relevant findings show an interaction effect between catering and firm’s characteristics, particularly high liquid assets, valuable investment opportunities, and higher levels of free cash flow. Research limitations/implications Given the subjectivity inherent in creating a variable that captures the sentiment of investors, the author admits that there are other variables to consider. Also, corporate governance factors could have been introduced as well as other countries with different institutional environments. Originality/value To the best of the author’s knowledge, this is a novel approach that incorporates a variable capturing investor’s sentiment at the firm level. With the approach suggested it has been shown that investors’ sentiments impact dividends payout, highlighting its usefulness for managers who are expected to pay dividends according to investors’ expectations. Moreover, this work also demonstrated that firm’s characteristics could affect the investor sentiments for dividends also conveying a valuable contribution for investors.
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Mušanović, Jelena, Jelena Dorčić, and Tea Baldigara. "Sentiment analysis of social media content in Croatian hotel industry." Zbornik Veleučilišta u Rijeci 9, no. 1 (2021): 37–57. http://dx.doi.org/10.31784/zvr.9.1.3.
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While social media have become a daily routine in modern society, brand communication and engagement with customers have become essential elements of marketing strategy and success in the tourism and hotel industry. This revolution of social media, in tourism and hospitality marketing, contributed to the rise of a novel sentiment analysis from a machine learning and natural language processing point of view. The purpose of the study is: to provide a general descriptive overview of comments posted by Facebook page followers; to identify specific textual attributes of hotel brand posts on social media and to apply the sentiment analysis to Facebook comments from four- and five-star hotel brands in Croatia to identify and compare customers’ feelings and attitudes towards the staff, services and products that hotel brands promote by posting messages on Facebook pages. To analyse hotel brand sentiments, the authors collected a total of 4,248 comments and 2,373 postings in English, German and Italian. The results showed that the comments on four- and five-star hotel brands expressed predominantly positive sentiments. Despite the positively oriented sentiments in the comments, Facebook page followers are predominantly passive users and do not tend to comment actively. The results can be used by marketers in the tourism and hospitality industry to plan their future social media communication strategies.
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Lutz, Chandler. "THE ASYMMETRIC EFFECTS OF INVESTOR SENTIMENT." Macroeconomic Dynamics 20, no. 6 (December 17, 2015): 1477–503. http://dx.doi.org/10.1017/s1365100514000996.
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We use the returns on lottery-like stocks and a dynamic factor model to construct a novel index of investor sentiment. This new measure is highly correlated with other behavioral indicators, but more closely tracks speculative episodes. Our main new finding is that the effects of sentiment are asymmetric: During peak-to-trough periods of investor sentiment (sentiment contractions), high sentiment predicts low future returns for the cross section of speculative stocks and for the market overall, whereas the relationship between sentiment and future returns is positive but relatively weak during trough-to-peak episodes (sentiment expansions). Overall, these results match theories and anecdotal accounts of investor sentiment.
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Lai, Kwun-Ping, Jackie Chun-Sing Ho, and Wai Lam. "Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis." International Journal of Knowledge-Based Organizations 11, no. 3 (July 2021): 29–45. http://dx.doi.org/10.4018/ijkbo.2021070103.
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The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.
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Chojnowski, Michał, and Piotr Dybka. "Is Exchange Rate Moody? Estimating the Influence of Market Sentiments With Google Trends." Econometric Research in Finance 2, no. 1 (April 3, 2017): 1–21. http://dx.doi.org/10.33119/erfin.2017.2.1.1.
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This paper proposes a novel method of exchange rate forecasting. We extend the present value model based on observable fundamentals by including three unobserved fundamentals: credit-market, financial-market, and price-market sentiments. We develop a method of sentiments extraction from Google Trends data on searched queries for different markets. Our method is based on evolutionary algorithms of variable selection and principal component analysis (PCA). Our results show that the extended vector autoregressive model (VAR) which includes markets' sentiment, shows better forecasting capabilities than the model based solely on fundamental variables or the random walk model (naïve forecast).
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Bahri, Shivani, Pranav Bahri, and Sangeeta Lal. "A Novel approach of Sentiment Classification using Emoticons." Procedia Computer Science 132 (2018): 669–78. http://dx.doi.org/10.1016/j.procs.2018.05.067.
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Kechaou, Zied, Ali Wali, Mohamed Ben Ammar, Hichem Karray, and Adel M. Alimi. "A novel system for video news' sentiment analysis." Journal of Systems and Information Technology 15, no. 1 (March 15, 2013): 24–44. http://dx.doi.org/10.1108/13287261311322576.
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Pelicon, Andraž, Marko Pranjić, Dragana Miljković, Blaž Škrlj, and Senja Pollak. "Zero-Shot Learning for Cross-Lingual News Sentiment Classification." Applied Sciences 10, no. 17 (August 29, 2020): 5993. http://dx.doi.org/10.3390/app10175993.
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In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.
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Mishra, Kritika, Ilanthenral Kandasamy, Vasantha Kandasamy W. B., and Florentin Smarandache. "A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis." Symmetry 12, no. 10 (October 18, 2020): 1715. http://dx.doi.org/10.3390/sym12101715.
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With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.
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Du, Lin, Wei Ran Xu, and Ping Yang Liu. "Sentiment Information to Vector, a more Automatic Approach for Sentiment Analysis." Advanced Materials Research 945-949 (June 2014): 3418–23. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3418.
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As sentence level sentiment analysis having been studied extensively, it has been proven that the syntactic structure of a sentence usually holds important information for sentiment analysis, especially for handling polarity reversal. However, the previous attempts of adopting such structural information mainly focus on making certain predefined rules which requires large linguistic expertise of the rule-maker,and the procedure itself is often manually labored and time consuming. To solve this problem, in this paper we propose a novel simple vector model to represent a sentence’s syntactic structure and its prior sentiment information uniformly and rapidly. Experiment results show that our proposed approach performs well in COAE 2013 dataset, and could also be used for machine learning algorithms to extract more distinguish features automatically.
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Görmez, Yasin, Yunus E. Işık, Mustafa Temiz, and Zafer Aydın. "FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis." International Journal of Information Technology and Computer Science 12, no. 6 (December 8, 2020): 11–22. http://dx.doi.org/10.5815/ijitcs.2020.06.02.
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Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.
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Wan, Hai, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, and Jeff Z. Pan. "Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9122–29. http://dx.doi.org/10.1609/aaai.v34i05.6447.
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Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.
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Hao, Ming C., Christian Rohrdantz, Halldor Janetzko, Daniel A. Keim, Umeshwar Dayal, Lars erik Haug, Meichun Hsu, and Florian Stoffel. "Visual sentiment analysis of customer feedback streams using geo-temporal term associations." Information Visualization 12, no. 3-4 (June 21, 2013): 273–90. http://dx.doi.org/10.1177/1473871613481691.
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Large manufacturing companies frequently receive thousands of web surveys every day. People share their thoughts regarding a wide range of products, their features, and the service they received. In addition, more than 190 million tweets (small text Web posts) are generated daily. Both survey feedback and tweets are underutilized as a source for understanding customer sentiments. To explore high-volume customer feedback streams, in this article, we introduce four time series visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cell–based sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers) and Twitter (e.g. from Kung-Fu Panda movie reviewers).
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Tran, Thien Khai, and Tuoi Thi Phan. "Towards a Sentiment Analysis Model Based on Semantic Relation Analysis." International Journal of Synthetic Emotions 9, no. 2 (July 2018): 54–75. http://dx.doi.org/10.4018/ijse.2018070104.
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Sentiment analysis is an important new field of research that has attracted the attention not only of researchers, but also businesses and organizations. In this article, the authors propose an effective model for aspect-based sentiment analysis for Vietnamese. First, sentiment dictionaries and syntactic dependency rules were combined to extract reliable word pairs (sentiment - aspect). They then relied on ontology to group these aspects and determine the sentiment polarity of each. They introduce two novel approaches in this work: 1) in order to “smooth” the sentiment scaling (rather than using discrete categories of 1, 0, and -1) for fined-grained classification, then extract multi-word sentiment phrases instead of sentiment words, and 2) the focus is not only on adjectives but also nouns and verbs. Initial evaluations of the system using real reviews show promising results.
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Ito, Tomoki, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita, and Kiyoshi Izumi. "Word-Level Contextual Sentiment Analysis with Interpretability." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4231–38. http://dx.doi.org/10.1609/aaai.v34i04.5845.
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Word-level contextual sentiment analysis (WCSA) is an important task for mining reviews or opinions. When analyzing this type of sentiment in the industry, both the interpretability and practicality are often required. However, such a WCSA method has not been established. This study aims to develop a WCSA method with interpretability and practicality. To achieve this aim, we propose a novel neural network architecture called Sentiment Interpretable Neural Network (SINN). To realize this SINN practically, we propose a novel learning strategy called Lexical Initialization Learning (LEXIL). SINN is interpretable because it can extract word-level contextual sentiment through extracting word-level original sentiment and its local and global word-level contexts. Moreover, LEXIL can develop the SINN without any specific knowledge for context; therefore, this strategy is practical. Using real textual datasets, we experimentally demonstrate that the proposed LEXIL is effective for improving the interpretability of SINN and that the SINN features both the high WCSA ability and high interpretability.
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Vo Ngoc, Phu. "Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System." International Journal of Computers Communications & Control 13, no. 3 (May 27, 2018): 408–28. http://dx.doi.org/10.15837/ijccc.2018.3.3044.
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We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.
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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "Analysing user sentiment of Indian movie reviews." Electronic Library 36, no. 4 (August 6, 2018): 590–606. http://dx.doi.org/10.1108/el-08-2017-0182.
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Purpose To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews. Design/methodology/approach An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest. Findings The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48. Research limitations/implications Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario. Practical implications In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers. Social implications The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications. Originality/value The constructed PCC is novel and was tested on Indian movie review data.
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Li, Liu, Zhang, and Liu. "An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism." Future Internet 11, no. 4 (April 11, 2019): 96. http://dx.doi.org/10.3390/fi11040096.
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Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.
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Bokaee Nezhad, Zahra, and Mohammad Ali Deihimi. "A COMBINED DEEP LEARNING MODEL FOR PERSIAN SENTIMENT ANALYSIS." IIUM Engineering Journal 20, no. 1 (June 1, 2019): 129–39. http://dx.doi.org/10.31436/iiumej.v20i1.1036.
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With increasing members in social media sites today, people tend to share their views about everything online. It is a convenient way to convey their messages to end users on a specific subject. Sentiment Analysis is a subfield of Natural Language Processing (NLP) that refers to the identification of users’ opinions toward specific topics. It is used in several fields such as marketing, customer services, etc. However, limited works have been done on Persian Sentiment Analysis. On the other hand, deep learning has recently become popular because of its successful role in several Natural Language Processing tasks. The objective of this paper is to propose a novel hybrid deep learning architecture for Persian Sentiment Analysis. According to the proposed model, local features are extracted by Convolutional Neural Networks (CNN) and long-term dependencies are learned by Long Short Term Memory (LSTM). Therefore, the model can harness both CNN's and LSTM's abilities. Furthermore, Word2vec is used for word representation as an unsupervised learning step. To the best of our knowledge, this is the first attempt where a hybrid deep learning model is used for Persian Sentiment Analysis. We evaluate the model on a Persian dataset that is introduced in this study. The experimental results show the effectiveness of the proposed model with an accuracy of 85%.
ABSTRAK: Hari ini dengan ahli yang semakin meningkat di laman media sosial, orang cenderung untuk berkongsi pandangan mereka tentang segala-galanya dalam talian. Ini adalah cara mudah untuk menyampaikan mesej mereka kepada pengguna akhir mengenai subjek tertentu. Analisis Sentimen adalah subfield Pemprosesan Bahasa Semula Jadi yang merujuk kepada pengenalan pendapat pengguna ke arah topik tertentu. Ia digunakan dalam beberapa bidang seperti pemasaran, perkhidmatan pelanggan, dan sebagainya. Walau bagaimanapun, kerja-kerja terhad telah dilakukan ke atas Analisis Sentimen Parsi. Sebaliknya, pembelajaran mendalam baru menjadi popular kerana peranannya yang berjaya dalam beberapa tugas Pemprosesan Bahasa Asli (NLP). Objektif makalah ini adalah mencadangkan senibina pembelajaran hibrid yang baru dalam Analisis Sentimen Parsi. Menurut model yang dicadangkan, ciri-ciri tempatan ditangkap oleh Rangkaian Neural Convolutional (CNN) dan ketergantungan jangka panjang dipelajari oleh Long Short Term Memory (LSTM). Oleh itu, model boleh memanfaatkan kebolehan CNN dan LSTM. Selain itu, Word2vec digunakan untuk perwakilan perkataan sebagai langkah pembelajaran tanpa pengawasan. Untuk pengetahuan yang terbaik, ini adalah percubaan pertama di mana model pembelajaran mendalam hibrid digunakan untuk Analisis Sentimen Persia. Kami menilai model pada dataset Persia yang memperkenalkan dalam kajian ini. Keputusan eksperimen menunjukkan keberkesanan model yang dicadangkan dengan ketepatan 85%.
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KIM, Jun Sik, Da-Hea KIM, and Sung Won SEO. "INDIVIDUAL MEAN-VARIANCE RELATION AND STOCK-LEVEL INVESTOR SENTIMENT." Journal of Business Economics and Management 18, no. 1 (February 5, 2017): 20–34. http://dx.doi.org/10.3846/16111699.2016.1252794.
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This research studies the effect of stock-level investor sentiment on individual stock returns’ mean-variance relation. Using unique buy and sell volume data of retail investors in Korean stock market, we find that a positive mean-variance relation is undermined among high-sentiment stocks, but holds among low-sentiment stocks. We adopt buy-sell imbalances of retail investors for individual stocks as a measure of stock-level investor sentiment. Further, our findings provide empirical evidence of a strong riskreturn trade-off among stocks with low retail concentration (e.g., large capitalization, high-priced, and growth stocks). Existing research only analyzes market-wide investor sentiment. However, we study the effect of stock-level investor sentiment on individual stock returns. Therefore, our findings suggest novel implications about the investment strategy that the stock-level investor sentiment is important when constructing portfolios based on variance.
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Salur, Mehmet Umut, and Ilhan Aydin. "A Novel Hybrid Deep Learning Model for Sentiment Classification." IEEE Access 8 (2020): 58080–93. http://dx.doi.org/10.1109/access.2020.2982538.
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Sun, Yunchuan, Mengting Fang, and Xinyu Wang. "A novel stock recommendation system using Guba sentiment analysis." Personal and Ubiquitous Computing 22, no. 3 (March 5, 2018): 575–87. http://dx.doi.org/10.1007/s00779-018-1121-x.
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Al-Sheikh, Eman S., and Mozaherul Hoque Abul Hasanat. "Social Media Mining for Assessing Brand Popularity." International Journal of Data Warehousing and Mining 14, no. 1 (January 2018): 40–59. http://dx.doi.org/10.4018/ijdwm.2018010103.
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Businesses seek to analyse their customer feedback to compare their brand's popularity with the popularity of competing brands. The increasing use of social media in recent years is producing large amounts of textual content, which has become rich source of data for brand popularity analysis. In this article, a novel hybrid approach of classification and lexicon based methods is proposed to assess brand popularity based on the sentiments expressed in social media posts. Two different classification models using Naïve Bayes (NB) and SVM are built based on Twitter messages for 9 different brands of 3 cosmetic products. In addition, sentiment quantification have been performed using a lexicon-based approach. Based on the overall comparison of the proposed models, the SVM classifier has the highest performance with 78.85% accuracy and 94.60% AUC, compared to 73.57% and 63.63% accuracy, 80.63% and 69.38% AUC of the NB classifier and the sentiment quantification approach respectively. Specific indices based on classification and lexicon approaches are proposed to assess the brand popularity.