advantages and disadvantages of sentiment analysis

Sarsam SM, Al-Samarraie H, Alzahrani AI, Wright B. Sarcasm detection using machine learning algorithms in Twitter: a systematic review. 10.1007/978-3-642-21802-6_57. 2020; Dau et al. (2021), Taboada (2016), Wang et al. Ain QT, Ali M, Riaz A, Noureen A, Kamranz M, Hayat B, et al. This shows that in the field of sentiment analysis, researchers have begun to reduce the attention they give to sentiment analysis of opinions on product or service quality, while still maintaining a certain degree of attention to "user review" and "online review." Sentiment analysis can help us interpret emotions in unstructured texts as positive, negative, or neutral, and even calculate how strong or weak the emotions are. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. (2020), From the point of view of the overall survey methodology, Angel et al. There have also been few survey works that leverages keyword co-occurrence analysis and community detection to analyze the connections between research methods and topics, and their evolution over time. Ombabi AH, Ouarda W, Alimi AM. One-third of customers say they will stop doing business with brands they love after just one bad experience. Momtazi S (2012) Fine-grained German sentiment analysis on social media. 4.1 and 4.2, we found that the research methods and topics of sentiment analysis are constantly changing. 2019). 2022; Feldman 2013; Habimana et al. The high-frequency keywords were presented in Table Table2.2. 2020); Ligthart et al. 2017; Santos et al. Li D, Qian J (2016) Text sentiment analysis based on long short-term memory. The informetric methods use natural language processing technologies to intuitively conduct topic mining and analysis of a large number of papers. The keyword community evolution diagram is shown in Fig. Figure 4. 2016; Zhang et al. 2015; Tai et al. Moreover, they have a great potential when applied to various domains or systems. Adak A, Pradhan B, Shukla N. Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: systematic review. A multi-label classification based approach for sentiment classification. Elo S, Kyngs H. The qualitative content analysis process. Kumar A, Sebastian TM. The reason may be that the research methods of C4 focus on "opinion mining" and "text mining," while those of C3 focus on "natural language processing" and "deep learning," and C3 provides more technical support for C4 research. 10.1109/FTC.2016.7821783. 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. 2021). For instance, if the people preparing the dictionary dont have sufficient domain knowledge, the method wont yield accurate results. In recent years, the popularity of social media has aroused increasing interest in sentiment analysis research, and the number of papers published, especially those related to different topics of sentiment analysis, has grown rapidly. 2020), and the application of some analytical models (Tan et al. 2012; Snchez-Rada and Iglesias 2019; Wang et al. 2020; Koto and Adriani 2015; Kumar, Akshi and Sebastian 2012; Medhat et al. Constructing bibliometric networks: a comparison between full and fractional counting. 2017; Alamoodi et al. 2015), improve the quality of products (Abrahams et al. (2021), Lin et al. Finally, we counted the number of keywords and removed meaningless terms like "sentiment analysis," "sentiment classification," and "sentiment mining.". Deng S, Xia S, Hu J, Li H, Liu Y. 2016; Jiang et al. C1, C2, C5, C6 communities: High-frequency keyword evolution diagram, C3, C4 communities: High-frequency keyword evolution diagram. The extremes on the spectrum usually correspond to positive or negative feelings about something, such as a product, brand, or person., When asked about the limitations of sentiment analysis, Russell said, Like all opinions, sentiment is inherently subjective from person to person, and can even be outright irrational. Fuzzy logic applied to opinion mining: a review. Businesses face the most complex technology landscape. and transmitted securely. 2021b); Alamoodi et al. In: 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), IEEE, p 19. Li W, Zhu L, Shi Y, Guo K, Cambria E. User reviews: sentiment analysis using Lexicon integrated two-channel CNNLSTM family models. Sentiment analysis of social media Twitter with case of anti-LGBT campaign in Indonesia using Nave Bayes, Decision Tree, and Random Forest Algorithm. Huang B, Ou Y, Carley KM (2018) Aspect level sentiment classification with attention-over-attention neural networks. (2018), Obiedat et al. (Ravi and Ravi 2015) also analyzed the early algorithms for sentiment analysis. 3.2 above. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. conducted an informetric analysis of research on opinion mining and sentiment analysis from 2000 to 2015 (Piryani et al. Lulu L, Elnagar A. Perianes-Rodriguez A, Waltman L, van Eck NJ. 8) and C6 (Fig. The advantages and disadvantages of sentiment analysis are summarized and analyzed, which lays a foundation for the in-depth research of scholars. Sentiment Analysis: The What & How in 2023 - Qualtrics 2021), sensitivity and specificity (Thakur and Deshpande 2019), etc. The paper extracts data from Twitter that report consumer conversations after the launch of new products in the videogame industry. In order to explore the application of sentiment analysis in building smart societies, Verma collected 353 papers published between 2010 and 2021 (Verma 2022). In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp 11081113. First, we imported the standardized bibliographic data into BibExcel. Sentiment analysis of a movie review can rate how positive or negative a movie review is and hence the overall rating for a movie. Sari IC, Ruldeviyani Y (2020) Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. Greater Income Potential. 2020; Piryani et al. Zucco C, Calabrese B, Agapito G, Guzzi PH, Cannataro M. Sentiment analysis for mining texts and social networks data: methods and tools. 10.3233/978-1-61499-264-6-353. Liu SM, Chen JH. 2019; Singh et al. 2010). (2021a, b); Alonso et al. ", The keyword co-occurrence network for C4 community. A unified approach to mapping and clustering of bibliometric networks. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. 2022; Wang et al. Systematic literature review of sentiment analysis on twitter using soft computing techniques. Aspect extraction for opinion mining with a deep convolutional neural network. Feel free to contact us if you have questions regarding sentiment analysis: For more in-depth knowledge on sentiment analysis, download our comprehensive whitepaper: Begm is an Industry Analyst at AIMultiple. Jain DK, Boyapati P, Venkatesh J, Prakash M. An intelligent cognitive-inspired computing with big data analytics framework for sentiment analysis and classification. However, deep learning technology still has room for improvement, and the hybrid methods combining sentiment dictionary and semantic analysis are gradually becoming a trend (Prabha and Srikanth 2019; Yang et al. Arulmurugan R, Sabarmathi KR, Anandakumar H. Classification of sentence level sentiment analysis using cloud machine learning techniques. 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. 2021; Khattak et al. Zhao N, Gao H, Wen X, Li H. Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. Peng H, Ma Y, Li Y, Cambria E. Learning multi-grained aspect target sequence for Chinese sentiment analysis. 2021; Raghuvanshi and Patil 2016). The keyword co-occurrence network for the C2 community. Yi J, Niblack W (2005) Sentiment Mining in WebFountain. Dangi D, Bhagat A, Dixit DK. Research on the relationship between public sentiment and stock prices has always been the focus of many scholars (Smailovi et al. Cambria E, Xing F, Thelwall M, Welsch R. Sentiment analysis as a multidisciplinary research area. 10.1109/DSAA.2015.7344882, Fink C, Bos N, Perrone A, Liu E, Kopecky J (2013) Twitter, public opinion, and the 2011 Nigerian Presidential Election. Bakar MFRA, Idris N, Shuib L (2019) An enhancement of Malay social media text normalization for Lexicon-based sentiment analysis. This has resulted in a lot of sentiment analysis studies focusing on COVID-19-related texts exploring the impact of the epidemic on peoples lives (Sari and Ruldeviyani 2020; Wang, T. et al. 2020). Precision, recall, accuracy and F1-score are the most commonly used evaluation metrics (Dangi et al. 2021). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. The keyword community network and the keyword community evolution are analyzed and visualized using these tools, as described in Sect. 2019; Yurtalan et al. Pros and Cons of NLTK Sentiment Analysis with VADER We can see that the C1 community and the C3 community have shown a significant growth trend. #2 Evaluate the power of a company's consumer network. 2021). Companies need a large or high-quality small dataset to have accurate classifications, Noise (e.g., emojis, slang, or punctuation marks) can reduce accuracy. A polarity calculation approach for lexicon-based Turkish sentiment analysis. Neural network models like LSTM (Al-Dabet et al. Li Y, Pan Q, Yang T, Wang S, Tang J, Cambria E. Learning word representations for sentiment analysis. 2020). Machine learning approaches have expanded from topic recognition to more challenging tasks such as sentiment classification. The main advantages and disadvantages of sentiment - ResearchGate Jiang D, Luo X, Xuan J, Xu Z. Text mining for market prediction: a systematic review. In addition, scholars have found that the consideration of user opinions can help improve the overall quality of recommender systems (Artemenko et al. A comparison of automated and lexicon-based sentiment analysis methods. 2022). The existing surveys conducted in-depth investigations of the contents and topics of sentiment analysis. (2019), and Zhou and Ye (2020), From the point of view of the methods of sentiment analysis, Acheampong et al. This field has many interrelated sub problems rather than a single problem to solve, which makes this field more challenging. We excluded papers on sentiment analysis related to image processing, video processing, speech processing, biological signal processing, etc. The existing surveys analyzed different methods of sentiment analysis. 2021; Elo and Kyngs 2008; Krippendorff 2018; Qazi et al. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work. In the study by Schouten et al., the authors focused on aspect-level sentiment analysis, combing the techniques of aspect-level sentiment analysis before 2014, such as frequency-based, syntax-based, supervised machine learning, unsupervised machine learning, and hybrid approaches. They summarized the methods and application prospects of sentiment analysis under different contents and topics. 2001). 10.1145/3430984.3431024. 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. With a large enough sample, outliers are diluted in the aggregate. When there is a large amount of literature to be surveyed, the use of the content analysis method and Kitchenham and Charters guideline requires more time and manpower. As research into sentiment analysis became more and more popular and there was important progress made in the development of deep learning technologies, researchers started to pay more attention to the techniques and methods of sentiment analysis. Awan MJ, Yasin A, Nobanee H, Ali AA, Shahzad Z, Nabeel M, et al. It is followed by "opinion mining," "natural language processing," "machine learning," and so on. The core content of the C5 community is "Arabic sentiment analysis." 2020b). 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 dedicated to the evolution of research methods and topics of sentiment analysis. Dec 2020 Mohammed Kaity Vimala Balakrishnan Building sentiment analysis resources is a fundamental step before developing any sentiment analysis model. They mainly reviewed the classification frameworks of the sentiment analysis process, supported language resources (dictionaries, natural language processing tools, corpora, ontologies, etc. 2021). Piryani R, Piryani B, Singh VK, Pinto D. Sentiment analysis in Nepali: exploring machine learning and lexicon-based approaches. 2020), Urdu (Khattak et al. Sentiment analysis on data from social media platforms related to COVID-19 has become a hot topic (Boon-Itt and Skunkan 2020). 10.21437/interspeech.2009-189. 2014). Wang ZY, Li G, Li CY, Li A. 10.18653/v1/2020a.acl-main.370. 2014). Smetanin S. The applications of sentiment analysis for Russian language texts: current challenges and future perspectives. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 110. 2018). 10.1109/IWBIS50925.2020.9255531. In addition, the number of keywords for "sentiment lexicon" and "lexicon-based" has declined. We believe that due to the impact of COVID-19, the widespread use of social platforms in 20202021 has led to a surge in the number of C1-related keywords. They are prone to human bias. After statistical analysis, we obtained 41,827 keywords with a total word frequency of 88,104. From this map, we can see that in the past two decades, research hotspots have included social media platforms (such as "social medium," "social network," and "Twitter"); sentiment analysis techniques and methods (such as "machine learning," "svm," "natural language processing," "deep learning," "aspect-based," "text mining," and "sentiment lexicon"), mining of user comments or opinions (e.g., "opinion mining," "user review," and "online review"), and sentiment analysis for non-English languages (e.g., "Arabic sentiment analysis" and "Arabic language"). Binkheder S, Aldekhyyel RN, Almogbel A, Al-Twairesh N, Alhumaid N, Aldekhyyel SN, et al. Sentiment analysis (SA) is an intellectual process of extricating user's feelings and emotions. Researchers can eliminate a large number of retrieved papers by using this standard process and finally conducting further analysis and research on a small number of papers. Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Rambocas M, Pacheco BG. Arabic sentiment analysis using recurrent neural networks: a review. 2014; Nassif et al. 10.1609/aaai.v24i1.7523, Li J, Sun M (2007) Experimental study on sentiment classification of chinese review using machine learning techniques. A literature review is formed as a result of the repeated use of this approach (Elo and Kyngs 2008; Stemler 2000). This section describes our proposed survey methodology, including collection of scientific publications, processing of scientific publications, as well as visualization and analysis using different methods and tools. These, combined with rules for affective reasoning to supplement interpretable information, will be effective in improving the performance of sentiment analysis. Accessibility Wang Z, Ho S-B, Cambria E. A review of emotion sensing: categorization models and algorithms. You can watch their video to grasp how your company can benefit from their services: Check our comprehensive article to learn more about crowdsourcing sentiment analysis and how it differentiates from traditional or automated methods. The keyword co-occurrence network features of the six sub-communities are described in Table Table4.4. Public sentiment and critical framing in social media content during the 2012 US Presidential Campaign. This paper has used keyword co-occurrence analysis and the informetric tools to enrich the perspectives and methods of previous studies. Visualization of how a support vector machine works. Xing FZ, Pallucchini F, Cambria E. Cognitive-inspired domain adaptation of sentiment lexicons. The usage of sentiment analysis in the domain of business intelligence has many advantages, for example, companies can exploit the results of sentiment analysis to make product improvements, study the customer's feedback, or adopt a new marketing strategy [51]. Singh T, Kumari M. Role of text pre-processing in twitter sentiment analysis. Plaza-del-Arco FM, Martn-Valdivia MT, Urea-Lpez LA, Mitkov R. Improved emotion recognition in spanish social media through incorporation of lexical knowledge. From the 573 papers retrieved in the initial search, they finally selected 37 papers to use in discussing sentiment analysis techniques (Kumar and Garg 2020). Arabic dialects are becoming increasingly popular as the language of informal communication on blogs, forums, and social media networks (Lulu and Elnagar 2018). 2021; Ain et al. The most notable advantages and disadvantages of these approaches are listed in Table 1. . 2021). In: Proceedings of the 53rd Annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, association for computational linguistics, pp 15561566. Tone Problem Tone can be difficult to interpret verbally, and even more difficult to figure out in the written word. Survey on sentiment analysis: evolution of research methods and topics 2020). 10.1007/978-3-642-16761-4_4. As the labeling is handled manually, data preparation can be time-consuming. Sentiment Analysis (image by Author) Sentiment Analysis, or Opinion Mining, is a subfield of NLP (Natural Language Processing) that aims to extract attitudes, appraisals, opinions, and emotions from text. 2020). Sentiment Classification on Big Data Using Nave Bayes and Logistic Regression. Pajek is a large and complex network analysis tool (Batagelj and Andrej 2022; Batagelj and Mrvar 1998). In: The 5th KES International Conference on Intelligent Decision Technologies (KES-IDT), Sesimbra, Portugal, pp 353362. Sentiment Analysis Methods in 2023: Overview, Pros & Cons - AIMultiple 2011). Under the influence of COVID-19, more people express their emotions, stress, and thoughts through social media platforms. When research on sentiment analysis was still in its infancy, the contents and topics of surveys mainly focused on sentiment analysis tasks, analysis granularity, and application areas. 2020; Binkheder et al. For example, Feldman summarized methods for extracting preferred entities from indirect opinions and methods for dictionary acquisition (Feldman 2013). On social media, blogs, and online forums millions of people are busily discussing and reviewing businesses, companies, and organizations. Abo MEM, Idris N, Mahmud R, Qazi A, Hashem IAT, Maitama JZ, et al. In the existing surveys, the researchers mainly conducted specific analyses of the tasks, technologies, methods, analysis granularity, and application fields involved in the sentiment analysis process. With the increase in the popularity of sentiment analysis research, more related research results began to accumulate. Tai KS, Socher R, Manning CD (2015) Improved Semantic Representations from Tree-Structured Long Short-Term Memory Networks. Therefore, to address the gaps in the existing surveys, this study presents a survey on the research methods and topics, and their evolution over time. Pereira DA. 2022). 2022a, b, c, d; Injadat et al. In C5 and C6, the research methods and topics are scattered. They analyzed and discussed sentiment analysis methods based on lexicons, rules, part of speech, term position, statistical techniques, supervised and unsupervised machine learning methods, as well as deep learning methods like LSTM, CNN, RNN, DNN, DBN, BERT, and other hybrid approaches (Acheampong et al. Du Y, He M, Wang L, Zhang H. Wasserstein based transfer network for cross-domain sentiment classification. Habimana et al. 2009). A survey on sentiment analysis methods, applications, and - Springer ), and different languages for sentiment analysis, such as Chinese, Spanish, and Arabic, etc. The keywords in the two communities are mainly related to the techniques and methods of sentiment analysis. 2021) are used to enhance the accuracy of machine learning, as shown in Fig. 2014). These included 3,809 articles, 5,633 proceeding papers, 267 reviews, and 5 pieces of editorial material from 2002 to 2022. 2019; Liu et al. Heikal M, Torki M, El-Makky N. Sentiment analysis of Arabic tweets using deep learning. It's a form of text analytics that uses natural language processing (NLP) and machine learning. 2012), and researchers are increasingly interested in the study of tweets and texts in the Arabic language (Heikal et al. In: 2020 International conference on Artificial Intelligence and Signal Processing (AISP), IEEE, p 16. Performance evaluation of DNN with other machine learning techniques in a cluster using apache spark and MLlib. 10.1007/978-3-319-69900-4_48. From the size of the circle, we can see that the keywords "domain adaptation"(Blitzer et al. Abrahams AS, Jiao J, Wang GA, Fan W. Vehicle defect discovery from social media. (2020). 10.48550/arXiv.2204.05783. Wang Z, Ho S-B, Cambria E. Multi-level fine-scaled sentiment sensing with ambivalence handling. 3.1 Collection of scientific publications above. Qasem M, Thulasiram R, Thulasiram P (2015) Twitter Sentiment Classification Using Machine Learning Techniques for Stock Markets. (PDF) LIMITATIONS OF SENTIMENT ANALYSIS ON FACEBOOK DATA - ResearchGate The .gov means its official. It is one of the pursued field of Natural Language Processing (NLP). Sentiment analysis methods based on lexicons, rules, part of speech, term position, statistical techniques, supervised and unsupervised machine learning methods, as well as deep learning methods like LSTM, CNN, RNN, DNN, DBN, BERT and other hybrid approaches have been analyzed and discussed. Preethi PG, Uma V, Kumar A. Temporal sentiment analysis and causal rules extraction from Tweets for event prediction. Network environment and financial risk using machine learning and sentiment analysis. Batagelj V, Andrej M (2022) Pajek [Software]. 2022; Cheng et al. A recent interview with Matthew Russell, co-founder and Principal of Zaffra discusses the limitations and possible applications of sentiment analysis. Advantages & Disadvantages Of Swot Analysis For Businesses Maqsood H, Mehmood I, Maqsood M, Yasir M, Afzal S, Aadil F, et al. Lin B, Cassee N, Serebrenik A, Bavota G, Novielli N, Lanza M. Opinion mining for software development: a systematic literature review. Alamoodi AH, Zaidan BB, Al-Masawa M, Taresh SM, Noman S, Ahmaro IYY, et al. (2018), and Zucco et al. However, there is a lack of adequate discussion on the connections between research methods and topics in the field, as well as on their evolution over time. 2021). Analytical mapping of opinion mining and sentiment analysis research during 20002015. In: 2018 International conference on Bangla Speech and language processing (ICBSLP), IEEE, pp 14. Find out your brand perception Build stronger customer relationships Offer better customer service Identify key emotional triggers Discover new marketing strategies Boost profits Manage crisis better Read on.. Zhou J, Ye J. What is Sentiment Analysis? Alonso MA, Vilares D, Gmez-Rodrguez C, Vilares J. followed Kitchenhams guideline and identified 14 secondary studies. Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. (2019), Kastrati et al. It is very important to explore and compare machine learning methods applied to sentiment classification (Li and Sun 2007). National Library of Medicine Al-Laith A, Shahbaz M. Tracking sentiment towards news entities from Arabic news on social media. In: Canadian conference on artificial intelligence, Springer, Berlin, Heidelberg, pp 286289. Leuven, Belgium: International Society for Scientometrics and Informetrics, pp 924. 2017), and Marouane Birjali (Birjali et al. SubjectAreas Education Keywords Deep Learning, Sentiment Analysis, Convolutional Neural Network, Recurrent Neural Network 1. Artemenko O, Pasichnyk V, Kunanets N, Shunevych K (2020) Using sentiment text analysis of user reviews in social media for E-Tourism mobile recommender systems. An overview of the most frequently used sentiment classification techniques. In: 2016 16th International Symposium on Communications and Information Technologies (ISCIT), IEEE, p 225229. It is a method that considers the frequent context that the words used. Table. 2020) have attracted more and more attention. 10.1007/978-3-642-35326-0_14, Koto F, Adriani M (2015) A comparative study on Twitter sentiment analysis: Which Features Are Good? 10.1109/ICCCI.2017.8117734. We found that combining the title and abstract could better reflect the core information. Review of Research on Text Sentiment Analysis Based on Deep Learning This analytical technique has gained broad acceptance not only among researchers but also among governments, institutions, and companies (Khatua et al. Most keywords are related to the research methods of sentiment analysis. In the context of the COVID-19 epidemic, Alamoodi et al. 10.1109/ICCUBEA.2017.8463638. Jingfeng Cui, Email: nc.ude.uajn@5004129102. (2020), Peng et al. While the words used frequently are closer to each other, the words rarely used together have a long distance between them. This review process is divided into six stages: research question definition, search strategy formulation, inclusion and exclusion criteria definition, quality assessment, data extraction, and data synthesis. Improving aspect-based sentiment analysis via aligning aspect embedding. An overview of the most frequently used sentiment classification techniques 1. Liu R, Shi Y, Ji C, Jia M. A survey of sentiment analysis based on transfer learning. They reviewed the tasks of sentiment analysis (e.g., different text granularity, opinion mining, spam review detection, and emotion detection), the topics of application areas of sentiment analysis (e.g. 2021; Zhou and Ye 2020) and service industries (Adak et al. 10.1007/978-3-642-40802-1_5. 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. Mao Y, Zhang Y, Jiao L, Zhang H. Document-level sentiment analysis using attention-based bi-directional long short-term memory network and two-dimensional convolutional neural network. 2019). Survey of Challenges in Sentiment Analysis | SpringerLink Fake News Detection Using Sentiment Analysis - IEEE Xplore 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. (2021), Boudad et al. Such key information is analyzed and visualized through different methods, including different visualization tools, as introduced in Sect. 1. Kaity M, Balakrishnan V. Sentiment Lexicons and non-English languages: a survey. It can calculate certain indicators to reveal the state and properties of the network involved. 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.
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