A smart local moving algorithm for large-scale modularity-based community detection. reviewed the basic terms, tasks, and levels of granularity related to sentiment analysis (Kumar and Sebastian 2012). We will present a specific analysis on the methods and topics of each sub-community in the next subsection. Medhat et al. 2021; Shofiya and Abidi 2021; Tan et al. Cross-domain sentiment classification is intended to address the lack of mass labeling data (Du et al. Wang T, Lu K, Chow KP, Zhu Q. COVID-19 sensing: negative sentiment analysis on social media in China via BERT model. Sentiment analysis is a useful tool for any organization or group for which public sentiment or attitude towards them is important for their success - whichever way that success is defined. 2020), Russian (Smetanin 2020), and Arabic (Alhumoud and Al Wazrah 2022; Ombabi et al. The most notable advantages and disadvantages of these approaches are listed in Table 1. . Yu J, Jiang J, Xia R. Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. The results in conferences are given the same relevance as journal papers. Finally, we counted the number of keywords and removed meaningless terms like "sentiment analysis," "sentiment classification," and "sentiment mining.". market, medicine, social media, election prediction, etc. 10.1609/aaai.v24i1.7523, Li J, Sun M (2007) Experimental study on sentiment classification of chinese review using machine learning techniques. This field has many interrelated sub problems rather than a single problem to solve, which makes this field more challenging. An evolutionary analysis of the associations between core contents is helpful for a comprehensive understanding of the research hotspots and frontiers in the field (Deng et al. 2020; Lo and Potdar 2009; Martinez-Camara et al. She holds a bachelor's degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services. Clickworker offers qualitative and quantitative sentiment analysis methods to meet clients needs. Cross-domain sentiment encoding through stochastic word embedding. As a result, a total of 685 representative keywords were reserved for subsequent analysis. Boon-Itt S, Skunkan Y. Wang C, Yang X, Ding L. Deep learning sentiment classification based on weak tagging information. Clickworker provides crowdsource sentiment analysis solutions to 70+ markets and is based in 158 countries worldwide. Agero-Torales MM, Salas JIA, Lpez-Herrera AG. Liu F, Zheng J, Zheng L, Chen C. Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. 2018). (2021), Angel et al. 2008; Leydesdorff et al. Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. The tool generates structured data files that can be read by Excel for subsequent processing (Persson et al. Keywords are the core natural language vocabulary to express the subject, content, ideas, and research methods of the literature (You et al. We excluded papers on sentiment analysis related to image processing, video processing, speech processing, biological signal processing, etc. It has attracted much attention (Du et al. They analyzed publication growth rates; the most productive countries, institutions, journals, and authors; and topic density maps and keyword bursts, among other elements. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, p 834840. A polarity calculation approach for lexicon-based Turkish sentiment analysis. Sentiment Scoring A comparison of automated and lexicon-based sentiment analysis methods. 2020; Garg 2021; Malandri et al. 2) is also presented in the end of Sect. Tone Problem Tone can be difficult to interpret verbally, and even more difficult to figure out in the written word. 10.1007/978-3-319-69900-4_48. To fill in the gap in existing research, we conduct keyword co-occurrence analysis and evolution analysis with informetric tools to explore the research hotspots and trends of sentiment analysis. Valverde-Albacete FJ, Carrillo-de-Albornoz J, Pelez-Moreno C (2013) A Proposal for New Evaluation Metrics and Result Visualization Technique for Sentiment Analysis Tasks. Bar-Ilan J. Informetrics at the beginning of the 21st centurya review. The contents of C5 and C6 may include some emerging research methods and topics. Evaluation metrics for quantifying the existing approaches are also a popular topic related to opinion mining. In: COLINS, CEUR-WS, Aachen, pp 259271. 7). According to [5], affective computing and sentiment analysis are the keys to the development of Artificial Intelligence (AI). 360 Degree view of cross-domain opinion classification: a survey. In: 28th International Conference on Machine Learning, International Machine Learning Society (IMLS), pp 513520. Snchez-Rada JF, Iglesias CA. The survey methods used have mainly been the content analysis method, Kitchenham and Charters' guideline, and the informetric methods. Advantages and disadvantages of the existing surveys, From the point of view of the contents and topics of sentiment analysis, Adak et al. Sayed AA, Elgeldawi E, Zaki AM, Galal AR (2020) Sentiment Analysis for Arabic Reviews Using Machine Learning Classification Algorithms. Binkheder S, Aldekhyyel RN, Almogbel A, Al-Twairesh N, Alhumaid N, Aldekhyyel SN, et al. JayaLakshmi ANM, Kishore KVK. First, we imported the standardized bibliographic data into BibExcel. 2022), Mean Absolute Error (MAE) (Yang et al. 10.48550/arXiv.1103.2903. Cai G, Xia B (2015) Convolutional neural networks for multimedia sentiment analysis. In: 2009 3rd IEEE International conference on digital ecosystems and technologies, IEEE, pp 396401. 3, the size of the node represents the number of keywords. (2018), Brito et al. A survey on sentiment analysis challenges. In: 2021 11th International conference on cloud computing, data science & engineering (confluence), IEEE, pp 175181. Yurtalan G, Koyuncu M, Turhan . It is followed by "opinion mining," "natural language processing," "machine learning," and so on. With this business, the sky is the limit in regards to your income potential. Martinez-Camara E, Martin-Valdivia MT, Urena-Lopez LA (2011) Opinion classification techniques applied to a Spanish Corpus. Exploring the topic structure and evolution of associations in information behavior research through co-word analysis. 2020; Picasso et al. As shown in Table Table4,4, we can see from the number of links between sub-communities that there is a strong correlation between them, especially the link between C3 and C4, which has 1306 lines. 2017), and Marouane Birjali (Birjali et al. The sentiment score of a text is determined by the following: Feel free to check our article on the top 5 sentiment analysis challenges and solutions. Schuller B, Mousa AED, Vryniotis V. Sentiment analysis and opinion mining: on optimal parameters and performances. 3.1 Collection of scientific publications above. Elshakankery K, Ahmed MF. 2021) and mental health (Yin et al. Cambria E, Xing F, Thelwall M, Welsch R. Sentiment analysis as a multidisciplinary research area. 2020), Urdu (Khattak et al. We collected research data from the Web of Science platform. They provided an overview of specific sentiment analysis tasks and of the features and methods required for different tasks (Ligthart et al. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey . Krippendorff K (2018) Content analysis: an introduction to its methodology. They are prone to human bias. From Fig. The use of the content analysis method and Kitchenham and Charters guideline enables in-depth analysis of literature contents. reviewed context-based sentiment analysis in social multimedia between 2006 and 2018. Performance evaluation of DNN with other machine learning techniques in a cluster using apache spark and MLlib. The main advantages and disadvantages of sentiment - ResearchGate In: 8th ACM IKDD CODS and 26th COMAD, association for computing machinery, p 163170. Sentiment Analysis: A Deep Dive Into the Theory, Methods, and An individuals sentiment toward a brand or product may be influenced by one or more indirect causes; someone might have a bad day and tweet a negative remark about something they otherwise had a pretty neutral opinion about. Besides, Schouten and Frasinar conducted a comprehensive and in-depth critical evaluation of 15 sentiment analysis web tools (Schouten and Frasincar 2015). In: 2012 IEEE International conference on computational intelligence and computing research, IEEE, pp 16. By adopting keyword co-occurrence analysis and community detection methods, we analyzed the research methods and topics of sentiment analysis, as well as their connections and evolution trends, and summarized the research hotspots and trends in sentiment analysis. Alonso MA, Vilares D, Gmez-Rodrguez C, Vilares J. The advantages and disadvantages of sentiment analysis are summarized and analyzed, which lays a foundation for the in-depth research of scholars. The main scheme includes four modules: Module A, Collection of scientific publications; Module B, Processing of scientific publications; Module C, Visualization and analysis through different methods and tools, and Module D, Result analysis and discussions based on various aspects. Xiong Z, Qin K, Yang H, Luo G. Learning Chinese word representation better by cascade morphological N-Gram. An individual's sentiment toward a brand or product may be influenced by one or more indirect causes; someone might have a bad day and tweet a negative remark about something they otherwise had a pretty neutral opinion about. Fake news data exploration and analytics. Shofiya C, Abidi S. Sentiment analysis on Covid-19-related social distancing in Canada using Twitter data. Analytical mapping of opinion mining and sentiment analysis research during 20002015. Researchers have been extracting text data from social media platforms for years to detect unexpected events (Bai and Yu 2016; Preethi et al. 2020; Dau et al. The granularity of sentiment analysis ranges from the early text level to the sentence level and finally to the aspect level, which is currently gaining strong attention. Public sentiment and critical framing in social media content during the 2012 US Presidential Campaign. Ain QT, Ali M, Riaz A, Noureen A, Kamranz M, Hayat B, et al. In: 2016 16th International Symposium on Communications and Information Technologies (ISCIT), IEEE, p 225229. 2020; Liu et al. A local and global event sentiment based efficient stock exchange forecasting using deep learning. 2022; Feldman 2013; Habimana et al. 2017), Arabic (Al-Ayyoub et al. 2019; Boudad et al. Sutoyo E, Rifai AP, Risnumawan A, Saputra M. A comparison of text weighting schemes on sentiment analysis of government policies: a case study of replacement of national examinations. Abdullah NSD, Zolkepli IA (2017) Sentiment analysis of online crowd input towards Brand Provocation in Facebook, Twitter, and Instagram. 3. A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: exploiting optimal machine learning algorithm selection. Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge. Thet TT, Na JC, Khoo CSG. 2022a, b, c, d; Injadat et al. 2014; Rotta and Noack 2011) and VOSviewer (Van Eck and Waltman 2010; VOSviewer 2021; Perianes-Rodriguez et al. Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. It has an advantage in subdividing different areas of study: multiple knowledge structures and details can be shown in one network (Deng et al. Therefore, future work should apply co-citation and diversity measures to explore the interdisciplinary nature of sentiment analysis research. Lin Y, Li J, Yang L, Xu K, Lin H. Sentiment analysis with comparison enhanced deep neural network. Taboada M. Sentiment analysis: an overview from linguistics. By categorizing sentiments in social media posts, surveys, or reviews, companies can measure how their strategies work and determine new ones for growth. Kumar A, Narapareddy VT, Gupta P, Srikanth VA, Neti LB, Malapati A (2021) Adversarial and auxiliary features-aware BERT for sarcasm detection. Wang Z, Chong CS, Lan L, Yang Y, Ho S-B, Tong JC (2016) Fine-Grained Sentiment Analysis of Social Media with Emotion Sensing. They reviewed the tasks of sentiment analysis (e.g., different text granularity, opinion mining, spam review detection, and emotion detection), the application areas of sentiment analysis (e.g. Answers to these questions may be found in the social media data. It can provide guidance for researchers, especially those who are new to the field, and help them determine research directions, avoid repetitive research, and better discover and grasp the research trends in this field (Wang et al. 2020; Peng et al. 2021). 2022; Cheng et al. Mntyl MV, Graziotin D, Kuutila M. The evolution of sentiment analysis-a review of research topics, venues and top cited papers. 10.1109/UKCI.2014.6930158. 2020). Janurio BA, de Carosia AEO, da Silva AEA, Coelho GP. Kastrati Z, Dalipi F, Imran AS, Nuci KP, Wani MA. 2021; Medhat et al. The number of papers each year from 2002 to 2021. Lexicon-based sentiment analysis methods are easily accessible as many publicly available resources (e.g.. analyzed various deep learning methods used in different applications at the level of sentence and aspect/object sentiment analysis, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) (Prabha and Srikanth 2019). Feldman R. Techniques and applications for sentiment analysis. A sentiment analysis model business has the advantage of a simple business model, which makes launching and building the business more seamless. These keywords appeared 25,429 times in the collected data, accounting for close to 83% of all the keywords. Zhang Y, Zhang Z, Miao D, Wang J. Three-way enhanced convolutional neural networks for sentence-level sentiment classification. Opinion mining has always been a hot field of research (Khan et al. Sentiment analysis is a Natural Language Processing (NLP) method that helps identify the emotions in text. Dictionary methods generate a dictionary by tagging words, and corpus-based methods involve the consideration of syntactic patterns. These methods are suitable for exploring research topics and trends in the field. C1, C2, C5, C6 communities: High-frequency keyword evolution diagram, C3, C4 communities: High-frequency keyword evolution diagram. 2021; Angel et al. Abo MEM, Idris N, Mahmud R, Qazi A, Hashem IAT, Maitama JZ, et al. However, the evolution of research methods and topics of sentiment analysis over time has not been studied with informetric methods. They reviewed the applications of sentiment analysis from the identified 28 articles, summarizing the adopted techniques such as dictionary-based models, machine learning models, and mixed models (Alamoodi et al. Picasso A, Merello S, Ma Y, Oneto L, Cambria E. Technical analysis and sentiment embeddings for market trend prediction. There are also a few authors who have used informetric methods to review papers. Sentiment Analysis Comparing 3 Common Approaches: Naive Bayes, LSTM Natural language based financial forecasting: a survey. With a large enough sample, outliers are diluted in the aggregate. 10.4304/jetwi.5.4.343-353, Khasawneh RT, Wahsheh HA, Al-Kabi MN, Alsmadi IM (2013) Sentiment analysis of Arabic social media content: a comparative study. Automated sentiment analysis methods include ML algorithms that categorize sentiment based on statistical models. The authors would like to thank the China Scholarship Council (CSC No. Advantages and challenges of social media and sentiment analysis Data mining techniques in social media: a survey. In examining the retrieved papers, we found that some paper topics, paper types, and publication journals were not related to sentiment analysis, so we excluded them. This method can reduce the amount of literature that requires in-depth reading, but in the case of a large amount of literature, more effort is still required to search and screen the material than in traditional literature review methods (Kitchenham and Charters 2007). 2020b; Hao et al. (2021), Nassirtoussi et al. We then combined the extracted keywords with the author keywords and removed duplicates. Content analysis is a powerful approach to characterizing the contents of each study by carefully reading its content and manually identifying, coding, and organizing key information in it. However, online text data is mostly unstructured. 4.1 and 4.2, we found that the research methods and topics of sentiment analysis are constantly changing. The main contributions of this survey are as follows: The remainder of this paper is organized as follows: In Sect. 2020). Rao G, Gu X, Feng Z, Cong Q, Zhang L (2021) A Novel Joint Model with Second-Order Features and Matching Attention for Aspect-Based Sentiment Analysis. Dangi D, Bhagat A, Dixit DK. We found that research hotspots include social media platforms, sentiment analysis techniques and methods, mining of user comments or opinions, and sentiment analysis for non-English languages. Such analysis can help structure user reviews, support product improvement decisions, discover public opinion hotspots, identify public positions, investigate user satisfaction with products, and so on. 2020), and the application of some analytical models (Tan et al. 2022). 2017). Machine learning algorithms used in sentiment analysis include. Sentence-level sentiment analysis is intended to perform sentiment analysis of the sentences in the document alone (Arulmurugan et al. (2014) and Ravi et al. 2021b; Asghar et al. Alhumoud SO, Al Wazrah AA. 2009; Nejat et al. We selected the high-frequency keywords under each category and plotted the change of word frequency in each year, as shown in Figs. 10.3115/v1/p15-1150, Tammina S (2020) A Hybrid Learning Approach for Sentiment Classification in Telugu Language. Companies need a large or high-quality small dataset to have accurate classifications, Noise (e.g., emojis, slang, or punctuation marks) can reduce accuracy. HHS Vulnerability Disclosure, Help Co-word analysis based on the frequency of co-occurrence of keywords used to describe papers can reveal the core contents of the research in specific fields. 2020a; Salur and Aydin 2020; Zhao et al. Trisna KW, Jie HJ. An example of hybrid sentiment classification. Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. 2015), p-value to statistically evaluate the relationship or difference between two samples of classification results (JayaLakshmi and Kishore 2022; Salur and Aydin 2020), paired sample t-tests to verify that the results are not obtained by chance (Nhlabano and Lutu 2018), and standard deviation to measure the stability of the model (Chang et al. (2021), Piryani et al. Things get even more complicated when one tries to analyze a massive volume of data that can contain both subjective and objective responses. In addition, scholars have found that the consideration of user opinions can help improve the overall quality of recommender systems (Artemenko et al. The informetric methods are best suited to investigating the research methods and topics of sentiment analysis (Bar-Ilan 2008; Mntyl et al. Nassirtoussi et al. 2015; Janurio et al. 2018). There are several methods to conduct sentiment analysis, each with its strengths and weaknesses. In: Communications in Computer and Information Science, Springer, Singapore, pp 342355. The aim of aspect-level sentiment analysis is to separately summarize positive and negative views about different aspects of a product or entity, although overall sentiment toward a product or entity may tend to be positive or negative (Rao et al. 2020; Zunic et al. 10.1109/IWBIS50925.2020.9255531. 2010). (2021a, b), Alonso et al. In opinion mining, researchers use many text mining methods to discover users opinions on goods or services, and then help improve the quality of corresponding products or services (Dau et al. Singh T, Kumari M. Role of text pre-processing in twitter sentiment analysis. In recent years, based on the success of deep learning technology, this method has gradually attracted attention.
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